# Harrington Starr — Head of AI Engineering

- Generated: 2026-09-13 05:26:53 PM EDT
- Original/canonical posting: https://www.linkedin.com/jobs/view/4464648826/
- Provider: LinkedIn
- Posted: approximately 2026-09-08 05:26 PM EDT (from '5 days ago')
- Elapsed since posting: represented by the provider's relative posting label and approximate Eastern timestamp
- Applicants: 80 applicants
- Work model/location: Boston, Massachusetts; eligible whether on-site, hybrid, or remote under Keith's Massachusetts criteria
- Compensation: $300,000-$400,000 base plus annual bonus
- Travel: Not disclosed
- Positioning track: Executive leader
- Fit outcome: PASS — 100%

## Direct-match strengths

Greenfield AI strategy, hands-on Python engineering, production LLM/RAG/agent systems, team building, evaluation, observability, model performance, vector databases, orchestration, AWS, governance, responsible AI, and financial-services experience.

## Hard or material gaps

The recruiter does not disclose the end employer or exact workplace cadence. Those are verification caveats rather than fit gaps because the role is located in Boston and the technical/leadership mandate is directly aligned.

## Evidence map

1. Own greenfield AI vision and strategy (weight 3, evidence 3/3) — Direct founder-led AI platform vision, architecture, roadmap, and delivery.
2. Build production LLM, RAG, and agent systems (weight 3, evidence 3/3) — Direct hands-on AssistX implementation.
3. Lead and grow AI engineers while hands-on (weight 3, evidence 3/3) — Built and technically led 11 engineers while coding and operating the platform.
4. Python AI application engineering (weight 3, evidence 3/3) — Direct Python, FastAPI, APIs, agents, RAG, and production systems.
5. Evaluation, observability, and model performance (weight 2, evidence 3/3) — Direct LLM-as-judge, regression evaluation, monitoring, cost, and reliability evidence.
6. Frameworks, vector databases, and orchestration (weight 2, evidence 3/3) — Direct LangChain, LangGraph, MCP, PostgreSQL/PGVector, and tool orchestration.
7. AWS or Azure (weight 2, evidence 3/3) — Deep AWS architecture and operations satisfy the either/or requirement.
8. Financial services or regulated environment (weight 1, evidence 3/3) — Direct MassMutual, governance, privacy, and regulated technology experience.

## Keyword diagnostic

Complete direct coverage of the greenfield AI strategy, hands-on production GenAI, player-coach team leadership, evaluation, observability, governance, and regulated financial-services mandate.

## Full normalized job description

Head of AI Engineering | Build an AI-First Platform from the Ground Up
I'm recruiting on behalf of an AI-native software company backed by an established financial services business. They're building a next-generation platform that's set to redefine how financial institutions create, manage and automate complex data integrations and transformations.
This isn't about adding AI to an existing product. AI is the product.
They're looking for a hands-on Head of AI Engineering to define the AI strategy, build and scale the AI Engineering function, and remain deeply involved in the technical direction. Whether you're already leading AI teams or you're a Principal AI Engineer ready to take that next step, this is a genuine opportunity to build something from day one and have a lasting impact on the business.
What You'll Do
Own the AI vision and technical strategy across a greenfield AI-native platform.
Design and build production-grade AI systems using LLMs, RAG and agentic architectures.
Lead and grow a team of AI Engineers while remaining hands-on with architecture and development.
Define best practices around evaluation, monitoring, governance and responsible AI.
Work closely with Product and Engineering leadership to shape the future of the platform.
What They're Looking For
Strong production experience building AI applications with Python, LLMs, RAG and agentic AI.
A player-coach mindset with experience leading engineers while staying close to the code.
Deep understanding of production AI, including evaluation frameworks, observability and model performance.
Experience with modern AI frameworks, vector databases, workflow orchestration and Azure or AWS.
Experience building AI products within financial services or another regulated environment is advantageous.
Why This Opportunity?
Build the AI capability from the ground up with real ownership over architecture and technical direction.
Join early enough to shape the product, engineering culture and future AI team.
Solve complex engineering challenges where accuracy, explainability and trust are critical.
Work on genuinely cutting-edge AI rather than incremental feature development.
Backed by an established financial services organisation, combining startup pace with long-term stability.
A rare opportunity to step into a highly influential leadership role while remaining deeply hands-on technically.
If you're looking for a role where you can define the AI vision, build a world-class engineering team and create technology that will shape the future of enterprise AI, I'd be happy to tell you more.

## Artifact metadata

- Resume: https://bit.ly/4gOBS14
- Cover letter: https://bit.ly/4yrZ3E8
- Validation: PASS — 2-page resume (915 words), 1-page cover letter (219 words); PDF geometry, bounds, annotations, links, and visual pages verified.
- Google Drive used: No