Sr Applied AI/ML Engineer

The Hartford•Hartford, CT

15h•Hybrid

About The Position

We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future. About Us: The Hartford is on a mission to create trustworthy, reliable, and transparent AIML systems, changing insurance and underwriting for good. We are driven by a strong determination to create a meaningful impact and take pride in being an insurance company that extends far beyond the realms of policies and coverages. When you choose to be a part of our team, you open the door to endless opportunities for personal and professional growth, as well as the chance to empower others in reaching their aspirations. You will help bring the transformative power of Generative AI capabilities, combine it with traditional ML to re-imagine the ‘art of possible’ and serve our internal customers and transform the business. Overview of the Role: We believe Generative AI is another tool in our arsenal and modern application needs a solution that unifies both traditional ML and LLM powered cognitive AI into one continuum. Our technology, platform, people and process fully embrace this strategy. We are seeking an AI ML Engineer who will be responsible for architecting, building and deploying production-grade AI systems This is a highly hands-on role requiring deep expertise in ML engineering, MLOps, LLM architecture, and Generative/Agentic AI concepts and tooling exposure. Someone with a diverse background/experience and an engineer at heart will fit into this role easily. Designs and develops AI/ML solutions to drive transformation and improve processes in underwriting, claims, operations, and corporate functions. Collaborate with Data Science Practitioners, LOB IT leads, EA, Data, and AI architects to develop solutions and integrate into operational processes and systems supporting various functions. Design, build and maintain scalable Agentic AI systems, including multi-agent workflows, remote Agent orchestration, tool calling, and human-in-the-loop (HITL) feedback. Implement Evaluation-driven development harness, grading logic, rubrics for evaluating AI Agents and tuning it for quality, safety, and reliability. Build full stack AI Agents with latest Agentic AI/UI frameworks & standards. Such as A2A, AAIF, A2UI, Agent skills, and MCP. Contribute to our starter packs (ADK/MCP), Horizontal Agents, and SDKs to tailor and deploy solutions across various use cases accelerating time to market. Apply advanced context engineering techniques like context splitting, advanced coordination, UX negotiation, checkpointing, and context offloading to build complex multi-agent systems using A2A and A2UI and MCP. Collaborate with AIOps, Platform, and Cloud teams to set up infrastructure and deploy AI Apps. Develop advanced RAG systems, such as Agentic RAG, GraphRAG etc to enhance accuracy and relevancy. Instrument AI observability using OpenTelemetry (OTel) tooling. Set up offline evaluation (LLM-as-a-judge, RAGAS scoring, ROUGE/BLEU where applicable), drift monitoring and playbacks in our Observability platform. Build scalable, fault-tolerant solutions on AWS and/or GCP in a multi cloud ecosystem.

Requirements

Strong technical knowledge (AI solution leveraging Cloud and modern solutions).
Able to communicate effectively with both technical and non-technical teams and influence leadership.
Collaboration across teams, decision making, conflict resolution and relationship building skills.
Experience in mentoring and developing Junior AI or ML Engineers.
Knowledge of latest AI and AI Agent architectural patterns.
Strong planning, organization, and execution skills.
Ability to understand and align deliverables to the departmental and organization strategies and objectives.
Ability to lead successfully in a lean, agile, and fast-paced organization, leveraging Scaled Agile principles and ways of working.
Innovative, curious, and focused on continuous learning, able to turn new ideas into practical solutions.
Result oriented, demonstrating ownership, accountability, and urgency in delivering business outcomes.
A bachelor’s or master’s degree in computer science, Software Engineering, Data Science, or a closely related discipline.
8+ years of experience in Machine Learning, Software Engineering or related field, with at least 5+ years focused on AI or ML
2+ years of experience in GenAI/Agentic AI, RAG, semantic search, embedding models, representation & generative models (BERT, GPT).
Strong programming skills in Python and experience with FastAPI, Asynio, & Pydantic.
Experience with single and multi-agent frameworks such as ADK, LangChain, LangGraph, and/or CrewAI.
Strong hands-on experience in GenAI AI tools and platforms, including AWS Sagemaker/Bedrock, Google Vertex AI, Vertex AI Search and Vertex AI RAG Engine.
Proven experience designing and delivering production-grade APIs, microservices & SDKs.
Experience with software engineering best practices (Ex. SOLID, 12 FACTOR App, Arch Sagas, Design patterns)
Experience in designing, building, and deploying data, ML, & RAG pipelines (FTI).
Demonstrated proficiency with agile development methodologies, including SAFe.

Nice To Haves

Experience with NodeJS , JavaScript, Typescript, React is a plus.
Experience with IAM, MSFT Entra, Okta, oAuth2.1, SAML, OIDC is a plus.
Understanding data structures, big data technologies (i.e. Hadoop, Spark, Hive, etc.), NoSQL and RDBMS is a plus.

Responsibilities

Designs and develops AI/ML solutions to drive transformation and improve processes in underwriting, claims, operations, and corporate functions.
Collaborate with Data Science Practitioners, LOB IT leads, EA, Data, and AI architects to develop solutions and integrate into operational processes and systems supporting various functions.
Design, build and maintain scalable Agentic AI systems, including multi-agent workflows, remote Agent orchestration, tool calling, and human-in-the-loop (HITL) feedback.
Implement Evaluation-driven development harness, grading logic, rubrics for evaluating AI Agents and tuning it for quality, safety, and reliability.
Build full stack AI Agents with latest Agentic AI/UI frameworks & standards. Such as A2A, AAIF, A2UI, Agent skills, and MCP.
Contribute to our starter packs (ADK/MCP), Horizontal Agents, and SDKs to tailor and deploy solutions across various use cases accelerating time to market.
Apply advanced context engineering techniques like context splitting, advanced coordination, UX negotiation, checkpointing, and context offloading to build complex multi-agent systems using A2A and A2UI and MCP.
Collaborate with AIOps, Platform, and Cloud teams to set up infrastructure and deploy AI Apps.
Develop advanced RAG systems, such as Agentic RAG, GraphRAG etc to enhance accuracy and relevancy.
Instrument AI observability using OpenTelemetry (OTel) tooling.
Set up offline evaluation (LLM-as-a-judge, RAGAS scoring, ROUGE/BLEU where applicable), drift monitoring and playbacks in our Observability platform.
Build scalable, fault-tolerant solutions on AWS and/or GCP in a multi cloud ecosystem.