# Harrington Starr — Hands-On Head of AI Engineering / Principal AI Engineer

## Source metadata

- LinkedIn posting: https://www.linkedin.com/jobs/view/4464654826/
- Posting company: Harrington Starr, recruiting for an unnamed capital-markets technology business
- Displayed title: Head of Engineering
- Role title in description: Hands-On Head of AI Engineering / Principal AI Engineer
- Location: Boston, Massachusetts
- Work model: Not stated
- Compensation: $220,000–$260,000 base, plus bonus and equity
- Applicant count: Fewer than 25 when captured on September 8, 2026
- Employment type: Full-time
- Posting time: Displayed as "3 hours ago" at approximately 6:25 PM Eastern on September 8, 2026; estimated posting time 3:25 PM Eastern
- Capture generated: September 8, 2026 at approximately 6:31 PM Eastern
- Elapsed time since posting at generation: Approximately three hours
- Recruiter: Jonny Potts, Principal Recruiter, Harrington Starr

## Role mission

Shape the next generation of an AI-first capital-markets platform while remaining deeply hands-on in architecture, engineering, deployment, and performance improvement.

## Responsibilities

- Design, build, and deploy production-grade AI applications.
- Architect scalable RAG pipelines, agentic workflows, and LLM-powered services.
- Build reliable AI APIs and backend services with Python.
- Drive model evaluation, prompt optimization, and AI-system performance.
- Partner with engineering leadership on architecture and technical strategy.
- Mentor engineers and establish best practices across the AI function.

## Requirements

- Production LLM application design and deployment.
- RAG, AI agents, and modern AI frameworks.
- Expert Python engineering.
- Vector databases, cloud platforms, and scalable distributed systems.
- Ownership of complex technical projects from concept through production.
- Financial-services or capital-markets experience.

## Fit assessment

- Decision: PASS
- Weighted fit: 95%
- Exact-function match: Direct. Keith has current hands-on production LLM, RAG, agent, API, evaluation, and cloud-platform experience.
- Level match: Direct as a principal-level player-coach with prior Head/VP/Director scope.
- Management-scope match: Direct. Keith has led teams of 7, 11, and 24 while retaining hands-on implementation ownership.
- Domain match: Strong adjacent/direct evidence through MassMutual, Invesco, and AWS financial-services customers.
- Platform/technology match: Direct Python, FastAPI, RAG, LangGraph, LangChain, PostgreSQL/PGVector, distributed cloud architecture, evaluation, guardrails, observability, APIs, AWS, Docker, and Kubernetes evidence.
- Work model/compensation: Compensation passes. Boston location is acceptable; the posting does not state the office cadence, so the work model must be confirmed.
- Applicant threshold: Passes; LinkedIn displayed fewer than 25 applicants.
- Strongest differentiators: Built and operates a production GenAI platform with 70+ automations across 20+ workflows; built 100% of the reusable core; guided 11 engineers; advised 200+ enterprises at AWS.
- Top gaps/caveats: Employer is confidential; specific capital-markets workflows and the required office cadence are not disclosed.

## Evidence map

- Production LLM applications: AssistX reached production in eight months and supports 70+ automations across 20+ workflows.
- RAG and vector databases: Built retrieval over labor-law sources from all 50 states using embeddings, PostgreSQL/PGVector, semantic search, grounding, and deterministic controls.
- Agentic workflows: Built multi-step workflows with model reasoning, tool execution, validation, evaluation, guardrails, observability, and human review.
- Python and AI APIs: Built the reusable AssistX core and initial HR product with Python, FastAPI, REST APIs, LangChain, and LangGraph.
- Cloud and distributed systems: Operated the AWS production stack and built an AgentCore/Strands agent deployed with AWS CDK; prior distributed-data architecture at AWS, TriMark, and MassMutual.
- Technical leadership: Guided 11 engineers at Intelligent DataWorks, led seven at SuccessKPI, and led 24 at NorthBay.
- Financial services: Delivered AI/analytics work for Invesco, led regulated engineering at MassMutual, and advised financial-services customers at AWS.

## Positioning track

Technical AI leader / principal-level player-coach. The resume emphasizes current implementation depth in the top third while retaining evidence of architecture ownership, mentoring, and engineering leadership.

## Artifact metadata

- Resume: Keith_Steward_Hands_On_Head_of_AI_Engineering_Resume.pdf (two pages, U.S. Letter, 0.4-inch margins)
- Cover letter: Keith_Steward_Hands_On_Head_of_AI_Engineering_Cover_Letter.pdf (one page, 244 words, U.S. Letter, 0.4-inch margins)
- Storage folder: Harrington_Starr
- Google Drive: Not used

## Source text

Hands On Head of AI Engineering

The recruiter is working with a well-funded capital-markets technology business building an AI-first platform for complex financial-services problems. The client seeks a Principal AI Engineer to shape its next-generation AI capability. This hands-on technical leadership role owns architecture, technical direction, and AI-product delivery while remaining deeply involved in engineering.

The role:

- Design, build, and deploy production-grade AI applications.
- Architect scalable RAG pipelines, agentic workflows, and LLM-powered services.
- Build reliable AI APIs and backend services using Python.
- Drive model evaluation, prompt optimization, and AI-system performance.
- Partner with engineering leadership on architecture and technical strategy.
- Mentor engineers and help establish best practices across the AI function.

What the client seeks:

- Experience building and deploying production LLM applications.
- Strong background with RAG, AI agents, and modern AI frameworks.
- Expert Python engineering skills.
- Experience with vector databases, cloud platforms, and scalable distributed systems.
- A track record of owning complex technical projects from concept through production.
- Previous experience within financial services or capital markets.

Why explore it:

- Join a business building AI at the core of its product.
- Hold substantial ownership and influence over technical direction.
- Solve challenging engineering problems with modern AI technologies.
- Work with experienced engineering leaders in a fast-paced environment.
- Competitive compensation, bonus, and equity.