Director of AI Product Engineering at People In AI

Job description

Director of AI Product Engineering (Agentic AI, Architecture & SaaS) Compensation: $240,000 base + approximately $20,000 bonus Location: Remote, US The Company An established, PE-backed B2B SaaS company operating in a regulated market is making AI a core part of its product strategy. The business has a substantial existing customer base, multiple production platforms, and a roadmap moving from relatively straightforward LLM applications toward more sophisticated agentic systems and AI infrastructure. The Opportunity This is a high-impact architecture role for someone who wants director-level technical influence without stepping away from the code. You will define how customer-facing AI is built across an established product portfolio while helping a broader engineering organization adopt stronger agentic, evaluation, context, and governance patterns. The Role You will own end-to-end architecture for the company’s customer-facing AI capabilities and lead a small senior AI team. This is explicitly a player-coach position: you will make major technical decisions, build reference implementations, work directly in the codebase, and enable product engineering teams to ship production AI safely at scale. What You’ll Do • Own architecture across agentic workflows, compliance-focused AI, content generation, retrieval, memory, context, and other customer-facing AI capabilities. • Design agent architectures, orchestration patterns, MCP integrations, reusable context standards, and reference implementations for wider engineering teams. • Establish rigorous evaluation methodologies and quality standards for production AI features. • Lead build-versus-buy decisions covering models, infrastructure, hosting, evaluation, and supporting AI platforms. • Partner with product and platform engineering teams to prototype capabilities, move them into production, and transfer ownership without becoming a bottleneck. • Define technical approaches to data governance, tenant isolation, model hosting, privacy, cost management, and enterprise AI requirements. • Coach engineers through architecture reviews, workshops, demos, and hands-on collaboration while continuing to write production code. What You’ll Bring • 10+ years of software engineering experience, including senior technical ownership of a production AI product. • Proven experience designing and shipping agentic systems involving orchestration, tool use, multi-step workflows, memory, or context management. • Current hands-on knowledge of LLM application architecture, retrieval, evaluation, prompt/context engineering, and production AI systems. • Experience operating at SaaS scale and improving existing products and architectures rather than working exclusively on greenfield systems. • Ability to communicate complex architecture clearly to engineers, product teams, executives, and non-engineering stakeholders. • A collaborative player-coach approach: comfortable setting technical direction while building relationships, mentoring teams, and working through regulatory or organizational constraints. What This Role Requires • 10+ years of software engineering experience with senior ownership of a production AI product. • Hands-on delivery of production agentic systems used by customers. • Current fluency across LLM application architecture and evaluation. • A genuine desire to remain hands-on in the code while operating at director level. Tech Stack • OpenAI and Anthropic models • Weaviate vector search • MCP and agent integration architecture • Retrieval, memory and context systems • Knowledge graphs and ontologies • AI evaluation infrastructure and methodologies • AWS Bedrock under consideration as part of the future hosting strategy Why Join • Own one of the most strategically important technical areas in the business, with meaningful authority over AI architecture across multiple product teams. • Work on real production challenges across agents, retrieval, knowledge graphs, evaluation, privacy, governance, and AI infrastructure rather than isolated prototypes. • Keep building while operating at leadership level, with the opportunity to raise the technical standard for AI across a substantial engineering organization. About People in AI People in AI is a specialist recruitment partner connecting exceptional AI, machine learning, data, and engineering talent with some of the most ambitious technology companies in the world. We work closely with founders, technical leaders, and hiring teams to represent opportunities accurately and help candidates assess genuine fit.

Requirements

Must-haves

Director of AI Product Engineering
(Agentic AI, Architecture & SaaS)
Location: Remote, US
What You’ll Do
Own architecture across agentic workflows, compliance-focused AI, content generation, retrieval, memory, context, and other customer-facing AI capabilities.
Design agent architectures, orchestration patterns, MCP integrations, reusable context standards, and reference implementations for wider engineering teams.
Establish rigorous evaluation methodologies and quality standards for production AI features.
Lead build-versus-buy decisions covering models, infrastructure, hosting, evaluation, and supporting AI platforms.
Partner with product and platform engineering teams to prototype capabilities, move them into production, and transfer ownership without becoming a bottleneck.
Define technical approaches to data governance, tenant isolation, model hosting, privacy, cost management, and enterprise AI requirements.
Coach engineers through architecture reviews, workshops, demos, and hands-on collaboration while continuing to write production code.
What You’ll Bring
10+ years of software engineering experience, including senior technical ownership of a production AI product.
Proven experience designing and shipping agentic systems involving orchestration, tool use, multi-step workflows, memory, or context management.
Current hands-on knowledge of LLM application architecture, retrieval, evaluation, prompt/context engineering, and production AI systems.
Experience operating at SaaS scale and improving existing products and architectures rather than working exclusively on greenfield systems.
Ability to communicate complex architecture clearly to engineers, product teams, executives, and non-engineering stakeholders.
A collaborative player-coach approach: comfortable setting technical direction while building relationships, mentoring teams, and working through regulatory or organizational constraints.
What This Role Requires
10+ years of software engineering experience with senior ownership of a production AI product.
Hands-on delivery of production agentic systems used by customers.
Current fluency across LLM application architecture and evaluation.
A genuine desire to remain hands-on in the code while operating at director level.

Nice-to-haves

Compensation: $240,000 base + approximately $20,000 bonus

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmawsmachine learning

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