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1d ago

AI Engineer

Bain & Co. New York, NY

New York, NY

1d ago

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

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Job Description

At Bain’s Coro team, this AI Engineer role focuses on building AI-infused software and data products that leverage LLM-driven features and agentic workflows. The work spans proof-of-concepts, MVP delivery, and scaled deployments, including retrieval and knowledge pipelines, MLOps and GenAIOps, and secure enterprise integration in real client environments.

This position is based onsite in New York, NY and supports end-to-end development across modern LLM stacks and ML systems, from translating client needs into technical requirements to operating production services with reliability, safety, and measurable performance.

Responsibilities

Design and develop GenAI applications such as copilots, workflow automation, and decision support for commercial teams using modern LLM stacks.
Implement agentic workflows where they provide clear value, including tool use, multi-step execution, and human-in-the-loop controls, with attention to reliability, safety, and clear failure modes.
Design and build advanced search, retrieval, and knowledge pipelines across diverse data structures and stores, including hybrid search, vector stores, graph databases / knowledge graphs, and traditional data platforms. Cover indexing strategies, metadata design, relevance tuning and reranking, freshness, caching, access controls, and source attribution.
Build robust agent capabilities covering context engineering, memory and state management (short-term and long-term), orchestration, routing, and tool integration patterns.
Integrate solutions into enterprise environments and workflows through APIs, data systems, and collaboration tools, balancing quality, latency, cost, privacy, and adoption.
Turn ambiguous client inputs into clear technical requirements, tradeoffs, and delivery plans.
Build ML solutions end-to-end: data preparation, feature engineering, model selection, training, validation and testing, and performance analysis.
Apply appropriate methods spanning classical ML and deep learning, including sequence, text, and image models when relevant.
Create reproducible training and evaluation pipelines using versioning, experiment tracking, robust validation, and clear documentation.
Demonstrate fluency with deep learning concepts including transformer fundamentals and LLM pre-training versus post-training (instruction tuning and preference optimization approaches).
Write clean, testable, maintainable code and ship AI services across the full SDLC: build, test, deploy, monitor, and iterate.
Apply MLOps and GenAIOps practices: CI / CD, reproducibility, environment parity, and model / prompt / agent versioning for operational readiness.
Build evaluation and observability for GenAI and agentic systems using tracing and instrumentation, regression test suites, automated scoring where appropriate, and iteration loops for prompt and policy optimization.
Design for secure enterprise deployment including access controls, auditability, data handling for sensitive and PII data, and responsible AI guardrails.
Build reusable components and accelerators such as templates, evaluation harnesses, connectors, and 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.
Work with Bain consultants to prioritize critical few technical decisions that unlock business value.
Support proposal shaping and scoping through 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 (e.g., REST / gRPC) and integrating with enterprise systems.
Hands-on experience building LLM-powered applications with delivery considerations including latency, cost, reliability, and security.
Experience building advanced retrieval / search systems (hybrid retrieval, vector search, reranking) and comfort working across multiple data stores including vector, graph, relational / document / search.
Experience implementing agentic patterns covering context management, tool integration, orchestration, and memory / state handling, with modern frameworks (e.g., LangGraph, OpenAI Agents SDK, Pydantic AI) or custom agent loops, and strong judgment about when agentic approaches are appropriate.
Experience creating reusable skills, tools, and services for agent use (including MCP), with schema validation (e.g., Pydantic) to enforce reliable data contracts.
Strong engineering practices including 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 equivalent orchestration) and operating services in production, including debugging, performance, and resilience.
Proven ability to implement security, privacy, and governance requirements including authentication / authorization, access controls, PII / sensitive data handling, and enterprise risk controls.
Experience training, validating, and testing ML models with 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 including classical ML and deep learning, and the ability to choose methods that match business and data constraints.
Familiarity with deep learning frameworks (e.g., PyTorch / TensorFlow) and ML lifecycle tooling such as experiment tracking and model registry / feature store concepts.
Proven ability to operate in ambiguity and complexity, manage priorities, and deliver outcomes independently or with a collaborative team.
Excellent interpersonal and communication skills for explaining technical decisions, tradeoffs, and results to mixed audiences.
Strong stakeholder management skills and comfort working directly with clients.

Technologies

English, Python, REST, gRPC
LLM stacks; LangGraph; OpenAI Agents SDK; Pydantic AI; Pydantic; MCP
AWS, GCP, Azure
Docker, Kubernetes
Vector stores, graph databases, knowledge graphs
Hybrid retrieval, vector search, reranking
Transformer fundamentals, instruction tuning, preference optimization approaches
PyTorch, TensorFlow
CI / CD, APIs, data pipelines

Benefits

Medical, dental and vision programs with 100% individual employee premiums.
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.
401(k) plan with an annual employer contribution based on years of service (4.5% 401(k) company contribution; increases after 3 years of service; 100% vested upon start date).
Annual discretionary performance bonus.

Preferred

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.

U.S. Compensation Information

Compensation includes base salary, annual discretionary performance bonus, 401(k) plan with an annual employer contribution based on years of service, and Bain’s best in class benefits package. In Massachusetts, New York, District of Columbia, Georgia, Illinois, Texas, Washington, and California, the good-faith, reasonable annualized full-time salary range is $128,500-$171,500, with placement varying based on factors such as experience, education, licensure/certifications, training, and skill level. This role may also be eligible for other elements of discretionary compensation, and includes a 4.5% 401(k) company contribution that increases after 3 years of service and is 100% vested upon start date.

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2d ago

Applied AI Engineer II - Encore Program

Deloitte Hermitage, TN

Hermitage, TN

2d ago

$103k - $189k/yr Full time Backend Developer Agentic Ai Ai Engineer

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Job Description

Build and enhance high-visibility full-stack products with applied AI in Deloitte’s Encore Program. This onsite role in Hermitage, TN focuses on outcome-focused delivery, technical leadership, and cost-aware engineering across the full lifecycle from discovery through production and operations. You will work with empowered cross-functional teams and contribute to a model that emphasizes scalable, cost-effective value creation.

Responsibilities

Drive a culture of accountability for customer and business outcomes, including the cost of achieving them.
Design and build lean, high-quality engineering solutions to address complex problems, while owning inference, token, and cloud costs for what you deliver.
Act as a technical advocate for products by ensuring code integrity, feasibility, and alignment with business and customer goals.
Participate across the delivery lifecycle, including requirement analysis, component design, development, testing, integrations, and support.
Maintain accountability for code-design integrity, implementation fidelity to the architecture and tech stack, plus quality, data, and ongoing maintenance and operations.
Stay hands-on and self-driven by continuously learning new approaches, languages, and frameworks.
Create technical specifications and write scalable, supportable code that meets or exceeds quality KPIs.
Use rapid, inexpensive experimentation to develop solutions that address customer needs.
Engage with customers and product teams before, during, and after delivery to support the right solution at the right time.
Adopt an action-and-evidence mindset to navigate complexity and uncertainty with a leaning-forward approach.
Collaborate with empowered teams across product management, experience, and delivery, incorporating diverse perspectives to balance feasibility, viability, usability, and value.
Apply expertise in modern software engineering and AI and Agentic SSDLC to support daily product deployments with automation across discovery, production, and operations, including quality checks through the SSDLC lifecycle.
Translate business and user needs, architectures, and UX/UI designs into technical specifications and code.
Communicate complex technical concepts clearly and influence teams with evidence-based trade-offs and coherent technical-to-business narratives.

Requirements

Bachelor’s degree in computer science, software engineering, data science, machine learning, or a related discipline. Experience is the most relevant factor.
3+ years of experience with most of: Angular, React, NodeJS, Python, C#, .NET, Java, SQL/NoSQL, PyTorch, TensorFlow, LangChain, LangGraph, and unit testing frameworks.
2+ years building AI/ML and agentic applications, including hands-on GenAI with LLM integration (OpenAI, Anthropic, or open-source models), RAG pipelines, prompt engineering, vector databases, evaluations, and AI agent orchestration.
2+ years cloud-native engineering with FaaS, PaaS, or micro-services on Azure, AWS, or GCP, including AI/ML services such as Azure OpenAI, AWS Bedrock, and Vertex AI, plus application-level infrastructure-as-code and cost-aware engineering (FinOps accountability).
Prior software engineering experience using Business Context Diagrams (BCD), sequence/activity/state/entity relationship/data flow diagrams, OOP/OOD, data structures, algorithms, and AI-augmented spec-driven development.
Experience with methodologies and tools such as XP, Lean, DevSecOps, SRE, ADO, GitHub, SonarQube, MLflow, and agentic AI frameworks (for example, LangFuse, LangSmith, or equivalent multi-agent orchestration tools).
Ability to travel 10% on average based on work with clients and industries/sectors.
Limited immigration sponsorship may be available.
Candidates must be located within a commutable distance to one of the select locations for this role.
Ability to work from your local office at least 3 days per week.

Technology Stack

Angular, React, NodeJS, Python, C#, .NET, Java, SQL/NoSQL, PyTorch, TensorFlow, LangChain, LangGraph, unit testing frameworks, OpenAI, Anthropic, LLM integration, RAG pipelines, prompt engineering, vector databases, AI agent orchestration, FaaS, PaaS, micro-services, Azure, AWS, GCP, Azure OpenAI, AWS Bedrock, Vertex AI, infrastructure-as-code, FinOps accountability, Business Context Diagrams (BCD), OOP/OOD, DevSecOps, SRE, ADO, GitHub, SonarQube, MLflow, LangFuse, LangSmith, XP, Lean, GenAI, Agentic SSDLC, AI-augmented spec-driven development.

Compensation

The estimated wage range for this role is $102,500 to $188,900 per year. Deloitte notes that hiring is not typical at or near the top of the range, and compensation depends on the facts and circumstances of each case.

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3d ago

Senior AI Engineer - FEA/CFD Surrogate Modeling

Honeywell Aerospace Torrance, CA

Torrance, CA

3d ago

Full time Ai Engineer Artificial Intelligence CFD

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Job Description

Honeywell Aerospace is seeking a Senior AI Engineer to advance AI-enabled engineering design and analysis through physics-based surrogate modeling. The role blends artificial intelligence with simulation workflows, helping accelerate aerospace mechanical system design using tools such as CFD, FEA, and multi-physics solvers.

Role Overview

In this Advanced AI Engineer position, you will develop and apply physics-informed machine learning surrogate models and Physics-AI approaches. The focus is on integrating AI with established computational methods to improve the speed and effectiveness of aerospace design and analysis activities, from early concept through integration into engineering workflows.

Key Responsibilities

Deliver technical contributions to the development of AI-driven engineering design tools and physics-based machine learning models.
Use artificial intelligence, physics-informed methods, and surrogate modeling to accelerate aerospace mechanical systems design and analysis.
Help define next-generation simulation and analysis capabilities by integrating AI with traditional computational tools including CFD, FEA, and multi-physics solvers.
Develop advanced AI surrogates and Physics-AI models.
Support the transition of these technologies into engineering workflows across Honeywell Aerospace.
Collaborate with cross-functional teams and global research partners.
Pursue internal and government research funding.
Contribute to execution from concept through integration.

Simulation and Modeling Technologies

The role centers on working with the following technologies:

CFD
FEA
multi-physics solvers

Location and Work Style

Torrance, CA (hybrid).

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Engineering Ai Data Jobs

AI Engineer

Applied AI Engineer II - Encore Program

Senior AI Engineer - FEA/CFD Surrogate Modeling

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