Graph Database

Graph Database Jobs

13 jobs found

Today

Data Engineer

Aristocrat Austin, TX

Austin, TX

Today

$91k - $169k/yr Full time Big Data Data Analytics Data Architecture

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Job Description

The Data Engineer role based in Austin, TX onsite focuses on building and maintaining scalable data pipelines, data models, and data integrity while collaborating with data scientists, analysts, and product teams to deliver analytical solutions.

Responsibilities

Design, construct, and sustain scalable data pipelines that pull data from casino source systems, apply transformations, and load optimized datasets for analytics and downstream applications.
Develop reliable production-grade code in Python and Node.js that meets reliability, performance, and maintainability standards.
Create and maintain data models that accurately reflect business domains and support analytical use cases.
Utilize ETL orchestration tools to schedule, monitor, and manage workflows in production environments.
Optimize queries and data processing logic to efficiently handle large volumes of transactional data.
Ensure data integrity across pipelines through validation, testing, and observability practices.
Identify and resolve data quality issues, pipeline failures, and performance bottlenecks.
Collaborate with data scientists, analysts, and product teams to understand data requirements and deliver solutions that meet them.
Document pipelines, data models, and technical decisions to support knowledge sharing and maintainability.

Requirements

4+ years of professional experience in data engineering or a closely related field.
Strong SQL skills and hands-on experience with data modeling for analytics or reporting use cases.
Proficiency in Python and Node.js for data processing and pipeline development.
Experience working with ETL orchestration platforms to develop, schedule, and manage production data workflows.
Solid understanding of ETL/ELT patterns and the trade-offs involved in crafting data pipelines at scale.
Familiarity with Linux environments for pipeline deployment and operational tasks.
Ability to work independently on well-defined problems, making sound technical decisions without close supervision.
Strong attention to detail and a genuine dedication to data quality and reliability.
Clear communicator who can explain technical decisions and data concepts to non-technical collaborators.
Experience with graph databases such as Neo4j is a plus but not required.

Technologies

Python
Node.js
SQL
Neo4j
Linux
ETL orchestration platforms

Benefits

Health, dental, and vision insurance
Paid time off
401(k) plan with employer matching

Why Aristocrat?

World Leader in Gaming Entertainment
Robust benefits package
Global career opportunities

Our Values

All about the Player
Talent Unleashed
Collective Brilliance
Good Business Good Citizen

Travel Expectations

None

Pay Range

USD 91,000 - 169,000 per year

Additional Information

This role is subject to mandatory background screening and regulatory approvals. As part of employment with Aristocrat, you may be required to complete a criminal background check, submit fingerprints, and obtain licenses or registrations with applicable gaming regulatory authorities.

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2d ago

Senior AI Engineer

Diverse Lynx Aguadilla

Aguadilla

2d ago

$50 - $55/hr Contract Senior Agentic Ai AI

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Job Description

Diverse Lynx is seeking a Senior AI Engineer to design, develop, and deploy Agentic AI solutions with a focus on autonomous multi-agent systems. This onsite contract role in Aguadilla, PR involves collaborating with business, data, and platform teams to deliver scalable, secure, production-grade Agentic AI platforms.

Location: Aguadilla, PR (onsite)

Job type: Contract

Salary: USD 50 - 55 per hourly

Responsibilities

Design and build Agentic AI systems including planning, reasoning, task execution, memory, and feedback loops
Develop multi agent architectures (task agents, reasoning agents, tool calling agents, orchestration agents)
Implement RAG, Knowledge Graph, and Memory based AI patterns for enterprise use cases
Integrate LLMs with enterprise systems, APIs, tools, and workflows
Define end to end AI solution architecture from data ingestion to inference and observability
Build reusable AI services, agent frameworks, and orchestration layers
Ensure scalability, resilience, performance, and security of AI solutions
Collaborate with cloud, data, and MLOps teams to productionize solutions
Define agent evaluation metrics (accuracy, autonomy, cost, latency, safety)
Embed Responsible AI guardrails including governance, explainability, auditability, and compliance
Work with product owners, domain SMEs, and architects to translate business problems into AI solutions
Mentor junior engineers and contribute to AI engineering standards and best practices
Support client discussions, solution walkthroughs, and technical decision making

Requirements

Strong proficiency in Python (mandatory)
Hands on experience with Agentic AI frameworks (e.g., LangGraph, LangChain, LlamaIndex, AutoGen, CrewAI or equivalent)
Experience with LLMs (OpenAI, Azure OpenAI, Anthropic, open source models)
Expertise in RAG, embeddings, vector databases, and prompt engineering
Experience building multi agent orchestration, tool calling, and workflow automation
Experience with vector databases (Pinecone, FAISS, Weaviate, Chroma, etc.)
Knowledge of knowledge graphs, ontologies, and semantic search
Cloud experience on Azure / AWS / GCP
Familiarity with APIs, microservices, event driven architectures
Model monitoring, evaluation, and cost optimization
8 12+ years of overall software / AI engineering experience
3+ years hands on experience in GenAI and emerging Agentic AI systems
Experience delivering production grade AI solutions in enterprise environments
Bachelor s or Master s degree in Computer Science, AI, Data Science, or related field

Technologies

Python
LangGraph
LangChain
LlamaIndex
AutoGen
CrewAI
OpenAI
Azure OpenAI
Anthropic
Pinecone
FAISS
Weaviate
Chroma
Azure
AWS
GCP
APIs
microservices
event driven architectures
Knowledge Graph

NICE TO HAVE

Experience in regulated industries (pharma, healthcare, life sciences)
Exposure to knowledge graphs, scientific/R&D workflows, or enterprise automation

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2d ago

Expert Consultant, Coro, AI Engineer

Bain & Co. Houston, TX

Houston, TX

2d ago

$129k - $172k/yr Full time Python Agentic Ai AI

Apply Now

Job Description

Join Bain & Company's Coro team as an AI Engineer to design and deploy GenAI powered features and agentic workflows from rapid proofs of concept to production, turning client data into structured analytics and synthesized outputs.

Responsibilities

Design and build GenAI applications including copilots, workflow automation, and decision-support for commercial teams using modern large language model stacks.
Implement agentic workflows with clear value, including tool use, multi-step execution, and human-in-the-loop controls, emphasizing reliability, safety, and clear failure modes.
Design and construct advanced search, retrieval, and knowledge pipelines across diverse data structures and stores (hybrid search, vector stores, graph databases/knowledge graphs, traditional data platforms) with indexing, metadata, relevance tuning, freshness, caching, access controls, and source attribution.
Develop robust agent capabilities such as context engineering, memory and state management, orchestration, routing, and tool integration patterns.
Integrate solutions into enterprise environments and workflows (APIs, data systems, collaboration tools) while balancing quality, latency, cost, privacy, and adoption.
Translate ambiguous client needs into clear technical requirements, tradeoffs, and delivery plans.
Deliver end-to-end ML solutions including data preparation, feature engineering, model selection, training, validation and testing, and performance analysis.
Apply methods spanning classical ML and deep learning, including sequence, text, and image models when relevant.
Create reproducible training and evaluation pipelines with versioning, experiment tracking, robust validation, and documentation.
Demonstrate fluency with modern deep learning concepts, transformer fundamentals, and LLM pre-training versus post-training concepts (e.g., instruction tuning and preference optimization).
Write clean, testable, maintainable code and ship AI services through the full SDLC: build, test, deploy, monitor, and iterate.
Implement MLOps and GenAIOps practices: CI/CD, reproducibility, environment parity, model/prompt/agent versioning, and operational readiness.
Build evaluation and observability for GenAI and agentic systems: tracing, instrumentation, regression test suites, automated scoring where appropriate, and iteration loops for prompts and policy optimization.
Design for secure enterprise deployment: access controls, auditability, data handling for sensitive and PII data, and responsible AI guardrails.
Build reusable components and accelerators (templates, evaluation harnesses, connectors, orchestration patterns) that scale across client contexts.
Communicate clearly with technical and non-technical stakeholders; lead working sessions, present recommendations, and write crisp technical documentation.
Collaborate with Bain consultants to prioritize critical technical decisions that unlock business value.
Support proposal shaping and scoping: effort sizing, architecture options, risk assessment, and delivery roadmaps.

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
3-5+ years of professional AI/ML engineering experience (or equivalent), with strong backend engineering fundamentals.
Strong proficiency in Python and experience building APIs / services (REST / gRPC) and integrating with enterprise systems.
Hands-on experience building LLM-powered applications with attention to latency, cost, reliability, and security.
Experience building advanced retrieval / search systems (hybrid retrieval, vector search, reranking) and working across data stores (vector, graph, relational / document / search).
Experience implementing agentic patterns (context management, tool integration, orchestration, memory / state handling) with modern frameworks (e.g., LangGraph, OpenAI Agents SDK, Pydantic AI) or custom agent loops, and sound judgment on when agentic approaches are appropriate.
Experience creating reusable skills, tools, and services (including MCP) for agent use, with schema validation (Pydantic) to enforce reliable data contracts.
Strong engineering practices: testing, code review, version control, CI/CD, and performance profiling.
Experience deploying and operating services on AWS, GCP, and / or Azure (environment management, reliability, observability, scaling).
Experience with Docker and Kubernetes (or equivalent orchestration) and operating services in production (debugging, performance, resilience).
Proven ability to implement security, privacy, and governance requirements for AI systems (authentication / authorization, access controls, PII / sensitive data handling, enterprise risk controls).
Experience training, validating, and testing ML models; strong understanding of overfitting, generalization, and evaluation methodology.
Practical experience with feature engineering and data preprocessing for real-world datasets.
Familiarity with a broad set of ML algorithms (classical ML and deep learning), and ability to choose methods that fit business and data constraints.
Familiarity with deep learning frameworks (PyTorch / TensorFlow) and ML lifecycle tooling (experiment tracking, model registry, feature store concepts).
Proven ability to operate in ambiguity and complexity, manage priorities, and deliver outcomes independently or with a team.
Excellent interpersonal and communication skills; able to explain technical decisions, tradeoffs, and results to mixed audiences.
Strong stakeholder management; comfortable working directly with clients.
MBA or PhD in a technical field.
Background in consulting, professional services, or B2B analytics environments.
Experience working with major AI ecosystem partners on real client deployments.

Technologies

Python
REST, gRPC
LangGraph
OpenAI Agents SDK
Pydantic AI
MCP
PyTorch
TensorFlow
Docker
Kubernetes
AWS, GCP, Azure
Graph databases, vector stores, knowledge graphs

Benefits

Bain pays 100% of individual employee premiums for medical, dental, and vision programs.
Generous paid time off, including parental leave, sick leave, and paid holidays.
Fully vested 401(k) company contribution.
Paid Life and Long-Term Disability insurance.
Annual fitness reimbursements.
4.5% 401(k) company contribution, increasing after 3 years of service and 100% vested upon start date.

Compensation

Base salary range: USD 128,500 - 171,500 per year. Compensation includes base salary, annual discretionary performance bonus, and a 401(k) plan with employer contributions based on years of service, plus Bain’s comprehensive benefits package. The 4.5% 401(k) company contribution increases after 3 years of service and is 100% vested from start date.

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3d ago

Data Engineer

Information Technology Senior Management Forum Bloomington, IL 🇺🇸 Remote

Bloomington, IL

3d ago

$153k - $153k/yr Full time Big Data Claims Data Pipelines Data Analysis

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Job Description

Data Engineer role focused on designing and implementing complex knowledge graph structures to enhance claims investigation and fraud detection through advanced graph analytics, with responsibilities spanning data engineering, ETL, DataOps, and cross-functional collaboration; option to work remotely in the U.S.

Responsibilities

Design and implement complex knowledge graph structures to bolster claims investigations and fraud detection via advanced graph analytics
Collaborate with cross-functional teams to drive data driven decision making and innovation
Apply industry standard languages and frameworks across coding, testing, security, DevOps, DataOps and data engineering practices with graph database technologies
Develop and maintain reusable, scalable, and compliant data solutions across multiple platforms and compute environments
Identify, acquire, cleanse, profile, and ETL data used in analytic discovery and production solution deployment across multiple platforms
Establish business domain knowledge for existing State Farm data sources and evaluate, recommend, and initiate data resource acquisitions, internal or external
Identify and consult on emerging technologies and critical core systems, including techniques, tools, data sources, and platforms in the data engineering field
Be familiar with handling datasets containing mixes of structured and unstructured data
Exhibit a DataOps mindset, ensuring data aligns to enterprise needs and leveraging automation to deliver quality data solutions
Collect and analyze information to identify customers' technical needs, propose solutions and develop implementation and integration plans (eg, technical proposals)
Be responsible for the analysis, design, deployment, support, and security of technology to ensure efficient management of technology and data-related assets in line with market best practices and external regulations
Apply a broad application of complex principles, theories, and concepts in computer science for data engineering solutions

Requirements

Bachelor’s degree or foreign equivalent in Computer Science or a closely related field
5 years of experience in the offered role or as a Specialist Programmer, or similar positions such as Machine Learning Engineer or Graph Data Engineer
5 years of experience with graph database management systems (Neo4j or Amazon Neptune) and knowledge of Cypher
5 years of experience with distributed computing frameworks, including Apache Spark
5 years of experience with programming languages, including Python or Java, and Scala
5 years of experience with containerization and orchestration, including Docker, Kubernetes, leveraging Graph Algorithms Libraries, and Neo4j GDS

Technologies

Neo4j
Amazon Neptune
Cypher
Apache Spark
Python
Java
Scala
Docker
Kubernetes
Graph Algorithms Libraries
Neo4j GDS

Benefits

Get Paid: eligible for annual raise and bonus on top of competitive pay
Stay Well: robust health and wellbeing programs; employer pays most healthcare premiums; multiple plans including high deductible; 100% in-network preventive care; vision, dental, telemedicine, 24/7 mental health support, and more
Develop and Grow: access to industry leading training, tuition assistance, employee resource groups, and mentoring
Plan Ahead: fertility/ IVF/ adoption assistance, college coaching, national discount programs, monthly financial workshops, free financial coaching, and savings account options or credit union access
Take a Little “You” Time: generous time off to plan around holidays, family events, volunteering; up to 20 days annually plus parental leave, paid holidays, celebration day, life leave (40 hours/year), bereavement leave, and community service/education support days
Give Back: Matching Gift Program, Good Neighbor Grant Program, and Employee Assistance Fund
Finish Strong: free financial planning and a 401(k) plan with company contributions up to 7%

Salary

USD 152,734 per year

Type

Full-time

Location

Bloomington, IL (remote)

Job ID

44799

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4d ago

Expert Consultant, Coro, AI Engineer

Bain & Co. Seattle, WA

Seattle, WA

4d ago

$129k - $172k/yr Full time Agentic Ai Ai Workflows Artificial Intelligence

Apply Now

Job Description

Join Bain Coro as an Expert Consultant, AI Engineer, delivering GenAI powered tools and agentic AI workflows for B2B Commercial Excellence. This onsite role in Seattle spans projects from proof-of-concept to production deployments, translating AI innovation into measurable business outcomes. The position offers a competitive annual salary of USD 128,500 to 171,500, and requires 3–5+ years of AI/ML engineering experience along with a Bachelor’s degree in Computer Science or Engineering, or equivalent practical experience.

Benefits

Health insurance (medical, dental, and vision) with Bain paying 100% of individual premiums
Generous paid time off, including parental leave, sick leave, and paid holidays
Fully vested 401(k) company contribution
4.5% 401(k) company contribution (vesting after 3 years)
Life and Long-Term Disability insurance
Annual fitness reimbursements
Annual discretionary performance bonus

Responsibilities

Develop AI-enabled tools and products that deliver measurable business outcomes
Design and implement GenAI applications such as copilots, workflow automation, and decision support for commercial teams using modern LLM stacks
Implement agentic workflows with emphasis on reliability, safety, and clear failure modes, including tool use, multi-step execution, and human-in-the-loop controls
Architect and build advanced search, retrieval, and knowledge pipelines across diverse data stores (hybrid search, vector stores, graph databases / knowledge graphs, and traditional data platforms), addressing indexing, metadata, relevance tuning, freshness, caching, access controls, and source attribution
Develop robust agent capabilities including context engineering, memory and state management, orchestration, routing, and tool integration patterns
Integrate solutions into enterprise environments and workflows (APIs, data systems, collaboration tools), balancing quality, latency, cost, privacy, and adoption
Translate ambiguous client needs into clear technical requirements, tradeoffs, and delivery plans
Build and apply data science and machine learning capabilities end-to-end: data preparation, feature engineering, model selection, training, validation and testing, and performance analysis
Apply methods across classical ML and deep learning, including sequence, text, and image models when relevant
Create reproducible training and evaluation pipelines with versioning, experiment tracking, robust validation, and clear documentation
Demonstrate fluency with modern deep learning concepts, including transformer fundamentals and LLM pre-training versus post-training approaches
Write clean, testable, maintainable code and ship AI services through the full SDLC: build, test, deploy, monitor, and iterate
Implement MLOps and GenAIOps practices: CI/CD, reproducibility, environment parity, model/prompt/agent versioning, and operational readiness
Build evaluation and observability for GenAI and agentic systems: tracing, instrumentation, regression test suites, automated scoring where appropriate, and iteration loops for prompt and policy optimization
Design for secure enterprise deployment: access controls, auditability, data handling for sensitive and PII data, and responsible AI guardrails
Build reusable components and accelerators that scale across client contexts
Thrive in a client-facing consulting environment: communicate clearly with technical and non-technical stakeholders, lead sessions, present recommendations, and document technical details
Collaborate with Bain consultants to prioritize critical technical decisions that unlock business value and support proposal shaping and scoping

Requirements

3–5+ years of professional AI / ML engineering experience (or equivalent), with strong backend engineering fundamentals
Strong proficiency in Python and experience building APIs / services (REST / gRPC) and integrating with enterprise systems
Hands-on experience building LLM-powered applications with attention to latency, cost, reliability, and security
Experience building advanced retrieval / search systems (hybrid retrieval, vector search, reranking), and comfort working across vector, graph, relational, document, and search data stores
Experience implementing agentic patterns (context management, tool integration, orchestration, memory/state handling) with modern frameworks (LangGraph, OpenAI Agents SDK, Pydantic AI) or custom agent loops
Experience creating reusable skills, tools, and services (including MCP) for agent use, with schema validation (Pydantic) to enforce data contracts
Strong engineering practices: testing, code review, version control, CI/CD, and performance profiling
Experience deploying and operating services on AWS, GCP, and / or Azure, with focus on reliability and observability
Experience with Docker and Kubernetes and operating services in production
Proven ability to implement security, privacy, and governance requirements for AI systems, including authentication/authorization and PII handling
Experience training, validating, and testing ML models; solid understanding of overfitting, generalization, and evaluation methodology
Practical experience with feature engineering and data preprocessing for real-world datasets
Familiarity with a broad set of ML algorithms (classical and deep learning) and the ability to select appropriate methods
Familiarity with deep learning frameworks (PyTorch / TensorFlow) and ML lifecycle tooling (experiment tracking, model registry, feature store concepts)
Ability to operate effectively in ambiguity and complexity, manage priorities, and deliver outcomes independently or with a team
Excellent interpersonal and communication skills, with the ability to explain complex technical decisions to mixed audiences
Strong stakeholder management and client-facing capabilities
MBA or PhD in a technical field (preferred)
Background in consulting, professional services, or B2B analytics environments (preferred)
Experience collaborating with major AI ecosystem partners on real client deployments (preferred)

Technologies

Python
REST
gRPC
LangGraph
OpenAI Agents SDK
Pydantic AI
PyTorch
TensorFlow
Docker
Kubernetes
AWS
GCP
Azure
Vector stores
Graph databases
Knowledge graphs

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4d ago

Expert Consultant, Coro, AI Engineer

Bain & Co. Washington, DC

Washington, DC

4d ago

$129k - $172k/yr Full time Python Advanced Analytics Agentic Ai

Apply Now

Job Description

Join Bain & Company in Washington, DC, onsite, as an AI Engineer on the Coro team. You will help build GenAI powered tools and data-driven capabilities for B2B Commercial Excellence, designing, developing, and deploying AI solutions from proofs of concept to production in a client-facing consulting setting. This role centers on agentic workflows, retrieval pipelines, and ML models that drive business value for our clients.

This position offers a compelling benefits package and a collaborative culture that supports growth, impact, and professional development. You will work closely with Bain consultants and client teams to deliver practical AI solutions, balancing quality, speed, privacy, and adoption in enterprise environments.

Responsibilities

Design and build GenAI applications such as copilots, workflow automation, and decision-support tools for commercial teams using modern large language model stacks.
Implement agentic workflows with value, including tool use, multi-step execution, and human-in-the-loop controls, emphasizing reliability, safety, and clear failure modes.
Design and construct advanced search, retrieval, and knowledge pipelines across diverse data stores, covering indexing, metadata, relevance tuning, freshness, caching, access controls, and source attribution.
Develop robust agent capabilities including context engineering, memory and state management, orchestration, routing, and tool integration patterns.
Integrate AI solutions into enterprise environments and workflows (APIs, data systems, collaboration tools) while balancing quality, latency, cost, privacy, and adoption.
Translate ambiguous client needs into clear technical requirements, tradeoffs, and delivery plans.
Deliver end-to-end ML solutions: data preparation, feature engineering, model selection, training, validation, testing, and performance analysis.
Apply suitable methods spanning classical ML and deep learning, including sequence, text, and image models where relevant.
Create reproducible training and evaluation pipelines with versioning, experiment tracking, robust validation, and documentation.
Demonstrate fluency with modern deep learning concepts, including transformer fundamentals and LLM pre-training versus post-training approaches.
Write clean, testable, maintainable code and ship AI services through the full SDLC: build, test, deploy, monitor, and iterate.
Implement MLOps and GenAIOps practices: CI/CD, reproducibility, environment parity, model/prompt/agent versioning, and operational readiness.
Build evaluation and observability for GenAI and agentic systems: tracing, instrumentation, regression test suites, automated scoring, and iteration loops for prompt and policy optimization.
Design for secure enterprise deployment: access controls, auditability, handling of sensitive and PII data, and responsible AI guardrails.
Develop reusable components and accelerators that scale across client contexts, including templates, evaluation harnesses, connectors, and orchestration patterns.
Communicate clearly with technical and non-technical stakeholders; lead working sessions, present recommendations, and document technical approaches succinctly.
Collaborate with Bain consultants to prioritize technical decisions that unlock business value and support proposal shaping and scoping.

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
3-5+ years of professional AI / ML engineering experience with strong backend fundamentals.
Proficiency in Python and experience building APIs / services (REST / gRPC) and integrating with enterprise systems.
Hands-on experience delivering LLM powered applications with attention to latency, cost, reliability, and security.
Experience building advanced retrieval and search systems (hybrid retrieval, vector search, reranking) and working across vector, graph, relational, document, and search data stores.
Experience implementing agentic patterns (context management, tool integration, orchestration, memory/state handling) with modern frameworks (LangGraph, OpenAI Agents SDK, Pydantic AI) or custom agent loops; solid judgment on when to apply agentic approaches.
Experience creating reusable skills, tools, and services for agent use, with schema validation (Pydantic) to enforce reliable data contracts.
Strong engineering practices: testing, code review, version control, CI/CD, and performance profiling.
Experience deploying and operating services on AWS, GCP, and/or Azure, including environment management, reliability, observability, and scaling.
Experience with Docker and Kubernetes (or equivalents) and operating services in production (debugging, performance, resilience).
Proven ability to meet security, privacy, and governance requirements for AI systems (authentication/authorization, access controls, PII handling, enterprise risk controls).
Experience training, validating, and testing ML models with solid understanding of overfitting, generalization, and evaluation methodology.
Practical experience with feature engineering and data preprocessing for real-world datasets.
Familiarity with a broad set of ML algorithms (classical and deep learning) and the ability to select methods aligned with business constraints.
Familiarity with deep learning frameworks (PyTorch/TensorFlow) and ML lifecycle tooling (experiment tracking, model registry, feature store concepts).
Ability to operate effectively in ambiguous and complex environments, manage priorities, and deliver outcomes independently or with a team.
Excellent interpersonal and communication skills; able to explain technical decisions and results to mixed audiences.
Strong stakeholder management skills and comfort working directly with clients.

Technologies

Python
REST, gRPC
LangGraph
OpenAI Agents SDK
Pydantic AI
Docker
Kubernetes
AWS, GCP, Azure
PyTorch, TensorFlow
MCP

Compensation and Benefits

Base salary range of USD 128,500 to 171,500 annually, with variation by location and experience for states including Massachusetts, New York, District of Columbia, Georgia, Illinois, Texas, Washington, and California.
Annual discretionary performance bonus.
4.5% 401(k) company contribution, increasing after three years of service, fully vesting on start date.
Bain pays 100% of individual employee premiums for medical, dental, and vision coverage.
Generous paid time off including parental leave, sick leave, and holidays.
Paid life and long-term disability insurance.
Annual fitness reimbursements.
Other comprehensive benefits and wellness programs in line with Bain’s offerings.

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9d ago

AI Engineer - GenAI / Agentic Systems - Financial Services

Infosys Basking Ridge, NJ

Basking Ridge, NJ

9d ago

$117k - $148k/yr Full time Agentic Ai AI Artificial Intelligence

Apply Now

Job Description

Responsibilities

Collaborate with business stakeholders in banking and financial services to identify problem statements and participate in or lead solution design, including understanding the day-in-the-life of an advisor and identifying critical manual flows.
Serve as Product Manager / Product Owner, working closely with architects and business SMEs to develop business requirements and AI solutions.
Design and develop GenAI solutions using LLMs (Claude, OpenAI, etc.) to improve advisor productivity and drive operational efficiency in areas such as Corporate Actions, Portfolio Management, AML / KYC processes.
Build agent-based workflows using frameworks like LangChain / Google ADK / similar tools.
Develop RAG pipelines, semantic search, and knowledge-driven applications.
Implement prompt engineering, evaluation frameworks, and guardrails to ensure reliable AI solutions.
Perform testing and validation of final solutions to align with business problems and optimize AI models and pipelines.
Coordinate with business and technology stakeholders to ensure program delivery.

Requirements

Experience in the financial services industry (capital markets / banking) and familiarity with front office, middle office, or back office operations or processes.
Understanding of the end-to-end value chain (wealth management, asset management, investment banking, etc.).
Experience defining AI-related KPIs and building an AI adoption business case.
Proficiency in Python-based AI/ML development.
Understanding of LLMs and GenAI architectures.
Prompt engineering and chaining techniques.
Data processing and pipeline development experience.
Understanding of Graph RAG and hands-on experience with LangChain / similar frameworks.
Hands-on experience with Claude / OpenAI / other LLM APIs for Graph RAG scenarios.
Working knowledge of vector databases, embeddings, and semantic search.
Working knowledge of API integrations and microservices.