Rag Architectures

Rag Architectures Data Jobs

68 jobs found

Today

Principal AI Engineer (SDLC)

AT&T Dallas, TX

Dallas, TX

Today

Full time Backend Developer Agentic Ai Ai Agent

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Job Description

AT&T seeks a Principal AI Engineer to design, build, and deploy production-ready generative AI and enterprise applications across the full SDLC.

Responsibilities

Design, develop, and deploy AI-powered applications and platforms aligned to enterprise business objectives
Build generative AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agent-based workflows
Develop intelligent assistants including copilots, chatbots, and AI automation solutions to improve employee and customer experiences
Turn AI concepts, proofs of concept, and prototypes into production-ready software products
Design and build scalable APIs, microservices, and backend services powering AI-enabled applications
Integrate AI capabilities into existing enterprise platforms, business systems, and customer-facing applications
Develop secure, reliable, and reusable application components within modern software architectures
Partner with cross-functional teams to translate business requirements into technical solutions
Deploy and manage AI workloads across cloud environments including Azure and AWS
Implement containerized and cloud-native solutions using Kubernetes and modern orchestration technologies
Build and maintain enterprise-grade AI platforms designed for high-volume production workloads
Optimize performance, reliability, security, and scalability of AI services
Establish and maintain CI/CD pipelines for AI-enabled applications and services
Apply MLOps best practices for deployment, monitoring, testing, and lifecycle management of AI solutions
Support model integration, version management, governance, and operational excellence
Monitor production environments and continuously improve platform performance and user experience
Evaluate emerging AI technologies and identify opportunities for enterprise adoption
Collaborate with product managers, software engineers, data scientists, UX teams, and business stakeholders
Contribute to technical architecture decisions and AI engineering best practices
Drive continuous improvement across AI development methodologies and delivery frameworks

Requirements

Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related technical field
Experience developing enterprise applications using modern software engineering practices
Strong proficiency in Python and modern application development frameworks
Experience building RESTful APIs and microservices
Knowledge of generative AI technologies including LLMs and AI application architectures
Experience with cloud platforms such as Azure and/or AWS
Experience working within Agile and SDLC environments
Knowledge of CI/CD pipelines, DevOps practices, and software release management
Experience with containerization and orchestration technologies such as Kubernetes and Docker
Strong problem-solving, analytical, and collaboration skills

Technologies

Python
RESTful APIs
microservices
Large Language Models (LLMs)
Generative AI
Retrieval-Augmented Generation (RAG)
agent-based workflows
Azure
AWS
Kubernetes
Docker
CI/CD pipelines
DevOps
MLOps
Agile
SDLC
vector databases
prompt engineering
AI evaluation frameworks
MLOps platforms
machine learning deployment practices

Example projects

Enterprise generative AI chatbots and virtual assistants
AI-powered recruiting and talent acquisition solutions
Customer care AI assistants powered by GPT and LLM technologies
Retrieval-Augmented Generation (RAG) platforms
Multi-agent and agentic workflow automation systems
AI APIs and shared enterprise AI services
AI-enabled operational intelligence and analytics platforms

Example at AT&T

Build a Network Operations Copilot that leverages multiple LLMs
Integrate with trouble-ticket and operational systems
Provide intelligent outage recommendations
Surface network analytics and operational insights
Deliver a fully deployed, production-ready software solution

Preferred qualifications

Experience building RAG (Retrieval-Augmented Generation) solutions
Experience developing agentic AI workflows and autonomous AI systems
Knowledge of MLOps platforms and machine learning deployment practices
Experience integrating AI solutions into enterprise business systems
Familiarity with vector databases, prompt engineering, and AI evaluation frameworks
Experience developing scalable cloud-native AI applications
Exposure to enterprise security, governance, and responsible AI practices

Ideal background

Candidates may currently hold titles such as AI Engineer, Generative AI Engineer, Machine Learning Engineer, AI Solutions Engineer, AI Application Engineer, or AI Platform Engineer

Location and schedule

Dallas, TX onsite
Location listing includes: Atlanta, Georgia and Dallas, Texas
Weekly hours: 40
Time type: Regular

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Today

AI Engineer

SOLIDWORKS Waltham, MA

Waltham, MA

Today

Full time Backend Developer Python Agentic Ai

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Job Description

Join SOLIDWORKS in Waltham, MA to build AI agents that connect directly with SolidWorks and help automate complex engineering workflows. This role focuses on turning multi-step reasoning into working CAD operations, from sketch creation to 3D model generation, and integrating those agents into design pipelines.

What you will do

Design and develop AI agents that can interact with SolidWorks through its APIs.
Create multi-step reasoning workflows where agents parse instructions, plan tasks, and execute CAD operations.
Integrate AI agents into client-server architectures using communication protocols such as WebSocket, REST, or similar approaches.
Implement automated generation of sketches and 3D models.
Debug, optimize, and improve agent workflows to ensure robust and accurate outputs.
Evaluate agent performance, tune prompts, and work to reduce hallucinations in structured engineering tasks.
Collaborate with mechanical engineers, UI/UX developers, and product definition to shape AI agentic workflows and implement them.

Requirements

Bachelor’s or Master’s Degree in Computer Science Engineering, IT, or a related engineering stream.
2–5 years of experience using C#, C++, Python, and AI technologies.
A solid understanding of software engineering principles and object-oriented programming.
Familiarity with CAD, visualization, simulation, or content creation applications (beneficial).
Interest or experience in AI/ML technologies for graphics, rendering, image generation, or workflow automation (plus).
Knowledge of other scripting languages is a plus.

Technologies

C#, C/C++, Python
LLMs, Embeddings, vector search, RAG systems
Agent frameworks (multi-step reasoning, tool use)
LangChain or LlamaIndex
OpenAI / Gemini / Claude APIs
Visual Studio, Git, Azure DevOps, Jira
WebSocket, REST
Cursor, VS Code

Benefits

Choice of plans providing comprehensive coverage for medical, dental, and vision for employee and dependents
Employee life
Short- and long-term disability
Tuition reimbursement
Immediate 401K enrollment
401K match: 50 cents on the dollar, up to the first 8% of eligible compensation (based on match eligibility criteria)
Flexible time off policy
10 paid holidays

Location: Waltham, MA (onsite)

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Today

AI Engineer, Ontologies & Knowledge Graphs

Cadence Design Systems Livonia, MI

Livonia, MI

Today

Full time Backend Developer Agentic Ai AI

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Job Description

Cadence Design Systems is seeking an AI Engineer to build knowledge graphs and retrieval-ready data pipelines that help make product capabilities programmatically understandable.

Responsibilities

Develop ETL/ELT pipelines to extract data from source code, APIs, file formats, and documentation and load it into a structured knowledge store
Design and maintain schemas and semantic data models for entities, relationships, and capabilities
Build and maintain knowledge graphs across heterogeneous product data
Create and maintain source and metadata parsers, including source-code/AST parsing for automated structure extraction
Implement typed programmatic interfaces and data-access layers on top of the knowledge layer
Deliver retrieval and indexing layers over product knowledge using embeddings and RAG
Partner with domain engineers to break complex product workflows into discrete, callable operations
Evaluate data sources for coverage, quality, and schema completeness across multiple products

Requirements

BS/MS in Computer Science, Mechanical Engineering, or similar
Strong Python and experience building and consuming REST APIs
Experience building data pipelines (ETL/ELT) across structured and unstructured data
Familiarity with graph databases and/or semantic/ontology modeling (RDF, OWL, property graphs, or equivalent)
Experience with at least one agent framework: LangChain, LangGraph, AutoGen, CrewAI, or similar
Understanding of how LLMs use context and call tools (retrieval, RAG, embeddings)
Exposure to CAE/FEA/CFD or a related physical-simulation or engineering domain
Comfortable working within unfamiliar or undocumented codebases
Systems thinking skills to decompose legacy workflows into discrete, callable steps

Technologies

Python, REST APIs, ETL/ELT
Graph databases; RDF, OWL, property graphs
LangChain, LangGraph, AutoGen, CrewAI
LLMs, retrieval, RAG, embeddings
CAE/FEA/CFD
AST parsing

Nice to Have

Vector databases
Data-access and API interface development
Parsing structured file formats
Surrogate modeling or related numerical methods

Additional Information

Role spans multiple products, building structured knowledge and interfaces over their capabilities
Works closely with domain engineers, who provide subject-matter expertise
Travel is not an expectation; occasional infrequent travel may occur for broad team alignment workshops

Location: Livonia, MI (onsite)

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Today

Senior AI/ML Data Engineer

Boeing Washington, DC

Washington, DC

Today

$242k - $305k/yr Full time Ai Ml Artificial Intelligence Data Architecture

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Job Description

Lead the design and delivery of enterprise-scale AI/ML data infrastructure enabling vector search, RAG, and agentic capabilities.

Responsibilities

Architect, build, and maintain enterprise-scale data platforms for vector databases, semantic search, Retrieval-Augmented Generation (RAG), Agentic AI systems, and large language model applications.
Define and implement controls for data quality, lineage, source attribution, and prompt and context traceability, including explainability and evaluation of AI outputs.
Drive architectural decisions for AI-enabled data infrastructure, balancing performance, scalability, security, reliability, maintainability, and cost.
Collaborate with Data Scientists, Machine Learning Engineers, Architects, Cybersecurity, and Software Engineers to translate AI requirements into production-grade capabilities.
Translate complex AI, machine learning, and data architecture concepts into clear operational impacts, risks, opportunities, and implementation considerations for senior leadership.
Coordinate across multiple organizations to align AI initiatives, maximize reuse of enterprise capabilities, and reduce duplicated effort.
Implement monitoring, observability, and alerting to support reliability, performance, and continuous platform improvement.
Provide technical leadership and mentorship, promoting engineering best practices and innovation across the organization.
Evaluate emerging AI technologies, including vector database platforms, retrieval frameworks, and engineering approaches to strengthen organizational AI capabilities.

Requirements

Active TS/SCI with CI Polygraph
Expert proficiency in Python, SQL, and modern software engineering practices
Deep experience with Azure, AWS, or Google Cloud data and AI platforms
Strong understanding of distributed systems, cloud-native architectures, MLOps, and AI platform engineering
Experience implementing vector databases, embedding pipelines, retrieval systems, and RAG architectures
Experience with CI/CD pipelines, orchestration platforms, infrastructure automation, and observability tooling

Technology Focus

Python, SQL
Azure, AWS, Google Cloud
Vector databases, embedding pipelines
Retrieval-Augmented Generation (RAG)
CI/CD pipelines, orchestration platforms, observability tooling

Education & Experience

Minimum experience: 20 years
Bachelor’s degree (may be substituted for 4 years of experience) or Master’s Degree (may be substituted for 6 years of e)

Desired Qualifications

Proven experience designing and delivering enterprise-scale AI, machine learning, generative AI, or Agentic AI solutions
Demonstrated success architecting and implementing production cloud-native data systems for advanced analytics and AI workloads
Proven experience working in complex enterprise environments with security, infrastructure, technology dependencies, governance requirements, and competing priorities
Extensive experience designing data pipelines for machine learning models, vector databases, semantic search, and generative AI applications
Proven experience delivering complex technical solutions from strategic requirements through operational deployment while balancing schedule, performance, capability, and cost objectives
Experience building and operating enterprise-scale vector search, RAG, knowledge management, and LLM-based platforms
Experience supporting AI adoption in large government, defense, intelligence, or highly regulated organizations

Work Model

Location: Washington, DC (hybrid)
Minimum onsite: 2-3 days onsite

Additional Location Details

Washington, DC or Reston, VA

Contingencies

Contingent upon program award

Compensation

Annual salary range: USD 242,000 - 305,000

Benefits

Generous company match to your 401(k)
Industry-leading tuition assistance program pays your institution directly
Fertility, adoption, and surrogacy benefits
Up to $10,000 gift match when you support your favorite nonprofit organizations

Application Privacy Policy

To review Boeing’s Privacy Policy, copy and paste: https://www.boeing.com/careers/privacy-statement

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Today

AI Engineer - FDE Software Engineering Sr. Manager

Accenture New York, NY

New York, NY

Today

$123k - $302k/yr Full time Backend Developer Manager Agentic Ai

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Job Description

Accenture is seeking an AI Native Engineer to embed with clients and help design, build, and deploy agentic workflows and AI-native systems in enterprise environments. The work spans end-to-end agent architecture, platform integration, and cloud-native engineering, with hands-on client engagement through POCs and code-with sessions.

What you’ll do

Design and engineer enterprise-ready AI agents including retrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability.
Build abstraction layers across AI providers such as Anthropic, Google, OpenAI, and others to support seamless integration and enablement.
Use containerization and cloud-native patterns including Kubernetes, Docker, microservices, serverless, and event-driven architectures, along with CI/CD and observability to deliver scalable AI-native systems.
Tailor and deploy agentic applications across verticals such as finance, healthcare, and retail, addressing domain-specific processes through intelligent automation.
Run design workshops, POCs, and code-with sessions to shape data-driven agent workflows with stakeholders and support trust and adoption.
Define and apply key metrics, test harnesses, and evaluation plans to assess accuracy, latency, safety, and cost effectiveness.
Create reusable patterns, documentation, and best practices that can influence internal assets and client roadmaps.

Requirements

10+ years of end-to-end software engineering experience and SDLC expertise.
8+ years with programming in Python, Java, or equivalent, plus familiarity with evaluation tooling, logging, monitoring, and agent observability.
8+ years deploying to production, including CI/CD, infrastructure as code (Terraform, Helm), monitoring, and debugging.
8+ years in client communication and collaboration, including leading technical workshops and delivering under ambiguity.
3+ years engineering cloud-native systems, including APIs, microservices, containerization, and serverless.
3+ years working with AI platforms such as OpenAI and Claude, and open-source models, including building abstraction layers to manage multi-provider pipelines.
2+ years hands-on experience designing and delivering Agentic AI solutions.
1.5+ years experience with semantic models, ontologies, knowledge graphs, or enterprise knowledge.
1.5+ years experience using coding agents and AI-assisted software development.
1+ year expertise designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments.
Bachelor’s degree or equivalent (minimum 12 years work experience). If Associate Degree, must have minimum 6 years work experience.

Technologies

Python, Java, Anthropic, Google, OpenAI, Claude
Kubernetes, Docker
Microservices, serverless, event-driven architectures
CI/CD, observability, Terraform, Helm
APIs, open-source models
Semantic models, ontologies, knowledge graphs
RAG, AI agents

Location and role details

Location: New York, NY (onsite)
Compensation: USD 122,700 - 302,400 per year
Travel: Travel may be required for this role, varying from 0 to 100% based on business need and client requirements.

Benefits

Medical, dental, vision, life, and long-term disability coverage
401(k) plan
Bonus opportunities
Paid holidays
Paid time off

Bonus points

Experience as an Agentic AI Engineer in an enterprise environment
Additional AI certifications or agentic tool experience
Experience defining or working with enterprise-grade architectures for compound AI systems, orchestration frameworks, or agent registry/stream-based architectures
Understanding of the AI-native paradigm, blending cloud-native with generative model architectures optimized for performance, modularity, and efficiency
Delivered solutions across multiple industries (for example, finance and healthcare) by tailoring agentic workflows to industry needs

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Today

AI Engineer - FDE Software Engineering Sr. Manager

Accenture Overland Park, KS

Overland Park, KS

Today

$123k - $302k/yr Full time Manager Agentic Ai Agentic Ai Orchestration

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Job Description

Accenture is hiring an AI Engineer to embed with enterprise clients as a technologist and trusted advisor. The role focuses on defining, prototyping, and deploying secure, operational agentic workflows, including architecture, AI platform integration, evaluation, and knowledge sharing.

Key Responsibilities

Design and engineer enterprise-ready AI agents, including retrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability.
Develop abstraction layers across AI providers such as Anthropic, Google, and OpenAI to support seamless integration and enablement.
Build scalable AI-native systems using containerization (Kubernetes, Docker), microservices, serverless, event-driven architectures, CI/CD, and observability.
Tailor and deploy agentic applications across verticals such as finance, healthcare, and retail, addressing domain-specific processes through intelligent automation.
Run design workshops, POCs, and code-with sessions to shape data-driven agent workflows with stakeholders and support trust and adoption.
Define and apply key metrics, test harnesses, and evaluation plans to measure agent accuracy, latency, safety, and cost effectiveness.
Create reusable patterns, documentation, and best practices that influence internal assets and client roadmaps.

Required Qualifications

Minimum 10 years of experience in end-to-end software engineering with SDLC expertise.
Minimum 8 years experience in:
- Programming in Python, Java, or equivalent, plus familiarity with evaluation tooling, logging, monitoring, and agent observability.
- Deploying to production, including CI/CD, infrastructure as code (Terraform, Helm), monitoring, and debugging.
- Client communication and collaboration, including leading technical workshops and delivering under ambiguity.
Minimum 3 years experience in:
- Cloud-native systems (APIs, microservices, containerization, serverless).
- AI platforms including OpenAI, Claude, and open-source models, including building abstraction layers for multi-provider pipelines.
Minimum 2 years hands-on experience designing and delivering Agentic AI solutions.
Minimum 1.5 years experience with semantic models, ontologies, knowledge graphs, or enterprise knowledge, and experience using coding agents and AI-assisted software development.
Minimum 1 year expertise designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments.
Bachelor’s degree or equivalent (minimum 12 years work experience). If an Associate Degree is held, a minimum of 6 years work experience is required.

Technologies

Python, Java, Anthropic, Google, OpenAI, Claude
Kubernetes, Docker, microservices, serverless
CI/CD, Terraform, Helm
Open-source models, semantic models, ontologies, knowledge graphs, RAG
APIs, event-driven architectures

Role Expectations

Acts as an AI Native Engineer with a minimum of 3 years of experience building cloud-native solutions and deep expertise in designing and deploying agentic systems for enterprise environments.
Applies critical thinking in ambiguous situations to deliver concrete outcomes by designing, building, and running custom AI agents that augment workflows.
Supports the development of a playbook for enterprises adopting and scaling AI-native engineering globally.

The Work

Embeds directly with clients as both a technologist and trusted advisor.
Partners with stakeholders to define use cases, rapidly prototype, and deploy agentic workflows that are robust, secure, and operational within complex enterprise domains.
Integrates agentic capabilities into new or existing platforms and systems, including stitching solutions together in clients’ environments alongside ecosystem partners.

Bonus Points (If Applicable)

Experience as an Agentic AI Engineer in an Enterprise environment.
Additional AI certifications or agentic tool experience.
Experience defining or working with enterprise-grade architectures for compound AI systems, orchestration frameworks, or agent registry/stream-based architectures.
Understanding of the AI-native paradigm combining cloud-native and generative model architectures, focused on performance, modularity, and efficiency.
Delivered solutions across multiple industries, including tailoring agentic workflows for industry needs (for example, finance and healthcare).

Compensation and Location

Location: Overland Park, KS (onsite)
Salary Range: USD 122,700 - 302,400 per year

Benefits

Medical, dental, vision
Life and long-term disability coverage
401(k) plan
Bonus opportunities
Paid holidays
Paid time off

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Today

Sr. SW AI Engineer

Visa Technology and Operations LLC Austin, TX

Austin, TX

Today

$111k - $172k/yr Full time Backend Developer Agentic Ai Ai Agent

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Job Description

Sr. SW AI Engineer role on Visa’s AI-first engineering team, building production-grade agentic systems powered by LLMs.

Responsibilities

Collaborate with stakeholders to interpret requirements for project components and incorporate feedback into future designs or solution fixes.
Verify assumptions while translating business requirements; escalate potential design issues to appropriate stakeholders.
Participate in system design and architecture, refining code plans and contributing to design documentation.
Support project estimation and escalate risks that may cause delays.
Develop, implement, and maintain code for products, services, or components using coding patterns, guidelines, and best practices.
Use debugging tools to validate assumptions and proactively flag issues before they occur.
Participate in code reviews to ensure coding standards are followed and handle routine pull requests.
Create test plans and configure testing procedures to identify and resolve defects across multiple features.
Respond to support requests during on-call rotations, troubleshooting issues and deploying fixes under guidance.
Leverage and build knowledge of software developer tools to create, debug, and maintain code for components.
Invest time in training resources to improve product availability, reliability, efficiency, observability, and performance.
Design, implement, and tune multi-agent LLM workflows using orchestration frameworks such as LangGraph, LangGraph4j, and LangChain4j, including declarative graph compilation, conditional routing, state persistence, and dynamic agent spawning.
Build and maintain ReAct agent loops (iterative Reason Act- Observe cycles) where an LLM selects tools, interprets tool results, and continues until a terminal answer is produced.
Implement Supervisor/Orchestrator agent patterns where a Lead Agent dynamically plans, spawns, and coordinates Specialist Sub-Agents using conditional graph edges and tool frameworks.
Develop MCP (Model Context Protocol) tool servers that expose enterprise data sources as callable tools for AI agents.
Implement and refine RAG pipelines: document ingestion and chunking (PDF/Word), embedding generation, vector storage (e.g., PostgreSQL + pgvector), and semantic similarity retrieval to ground responses in authoritative documents.
Integrate with enterprise LLM inference gateways via REST APIs, including request shaping, prompt template management, token budget control, and graceful degradation under service unavailability.
Design agent memory architectures: short-term per-agent isolated context stores and long-term shared knowledge repositories across workflows.
Implement confidence scoring and uncertainty quantification for AI-generated outputs, including threshold-based routing of low-confidence results to human review.
Build agent feedback loops by capturing human reviewer decisions and feeding structured corrections back into prompt templates and knowledge repositories.
Write LangChain4j / LangChain tool definitions (e.g., Java @Tool-annotated methods) and wire them into ReAct agents for data transformation, external API calls, and domain processing.
Build and maintain agentic audit trails with structured logs capturing tool calls, agent decisions, LLM prompt/response, and state transitions for traceability and explainability.
Develop and test Spring Boot microservices (Java 21, Spring Boot 3.5.x) to host AI orchestration engines, expose REST APIs, and integrate with upstream data source systems.
Author Helm charts and Jenkins CI/CD pipelines for containerized deployment of AI services to Kubernetes/OpenShift across multi-region on-premise data centers.
Develop and test end-to-end and integration tests for non-deterministic AI components, including prompt regression suites, tool call mock frameworks, agent output schema validators, and determinism gates to catch silent prompt drift.

Requirements

2+ years of work experience with a Bachelor’s Degree or an Advanced Degree (e.g., Masters, MBA, JD, MD, PhD).
OR 3+ years of work experience with a Bachelor’s Degree, OR more than 2 years with an Advanced Degree (e.g., Masters, MBA, JD, MD).
OR 2+ years relevant work experience with a Bachelor’s degree, OR 5+ years relevant work experience.
Experience in technologies/software systems or a directly related field (minimum two years).
Experience developing and/or implementing web-based or mobile applications (minimum two years).
Experience in system design and architecture for product components.
Experience in debugging and troubleshooting software issues.
Experience in code review and applying coding standards.
Experience in test planning and execution for software features.
Experience in responding to support requests and deploying fixes.
Experience using software developer tools for code creation and maintenance.
Experience developing backend services in Java (Java 17+ preferred; Java 21 a strong plus).
Familiarity with REST API design, Spring Boot, and JPA/ORM-based data access patterns.
Foundational understanding of LLM APIs: prompt construction, token limits, and response parsing.
Experience building and testing enterprise-scale web services (minimum one year).
Experience working on client-facing project or technical teams (minimum one year).
Experience integrating feedback into design and solution fixes.
Experience mentoring junior engineers and collaborating with cross-functional teams.
Hands-on experience building agentic AI systems using LangGraph, LangChain, LangGraph4j, or LangChain4j, including multi-agent graph construction, node/edge definitions, conditional routing, and state schema design.
Experience implementing the React prompting pattern (Thought Action- Observation) in a production or near-production LLM application.
Working knowledge of MCP: server registration, tool schema definition (tools/list, tools/call), and client-side integration.
Experience building RAG pipelines: document chunking strategies, embedding models, vector database querying (pgvector, Pinecone, Weaviate, or equivalent), and retrieval relevance tuning.
Experience with prompt engineering, including structured output enforcement (JSON schema), chain-of-thought prompting, few-shot example design, and system prompt management.
Experience building and monitoring LLM observability (token usage, latency per agent step, tool call success rates, output quality metrics).
Experience with PostgreSQL including pgvector for embedding storage and similarity search.
Experience deploying containerized workloads on Kubernetes or OpenShift, including Helm chart authoring, rolling deployments, and health probes.
Experience designing audit logging for AI agent decisions with structured, queryable formats capturing inputs, reasoning traces, tool calls, and outputs.
Familiarity with Aspect-Oriented Programming (AOP) for cross-cutting concerns in Spring Boot (agent call logging, latency measurement, security enforcement).
Experience with CI/CD pipelines (Jenkins or equivalent) for AI/ML service deployments, including version gating and environment promotion strategies.

Technologies

Generative AI tools (e.g., ChatGPT, Microsoft Copilot)
Large language models (LLMs)
LangGraph, LangGraph4j, LangChain4j, LangChain
ReAct (Reasoning + Acting)
Model Context Protocol (MCP)
REST APIs
RAG (Retrieval-Augmented Generation)
PostgreSQL, pgvector
JPA/ORM
Spring Boot, Java (Java 21), Spring Boot 3.5.x
Helm charts, Jenkins CI/CD pipelines
Kubernetes, OpenShift
PDF, Word
JSON schema
Aspect-Oriented Programming (AOP)
Pinecone, Weaviate

Benefits

Medical
Dental
Vision
401(k)
FSA/HSA
Life Insurance
Paid Time Off
Wellness Program

Work Details

Location: Austin, TX (onsite)
Salary: USD 110,700 - 171,800 per yearly
Work hours: Varies upon the needs of the department
Travel: 5-10% of the time
Work setting: Office setting; sit/stand at a desk; communicate in person and by telephone; frequently operate standard office equipment (telephones and computers)