Job Description

Founding AI Engineer (Agentic AI, Enterprise Data & Customer Ownership)

Seed Stage Revenue Positive Tech Company

Compensation: $200k–$300k base + equity

Location: US remote; hybrid for New York City-based candidates

The Company

A venture-backed applied AI company is building the infrastructure that allows AI agents to operate effectively inside complex enterprises. The business works with large organizations where critical knowledge and workflows are fragmented across data, software and operational systems, turning that complexity into production AI systems with measurable commercial value.

The company is still operating with a small, highly technical team despite significant early commercial traction, creating an unusual amount of ownership for every engineer joining now.

The Opportunity

This is not a narrow LLM integration role. You will combine AI engineering, backend development, data work, product judgement and direct customer exposure to take difficult operational problems from initial discovery through to production.

For someone who ultimately wants founder-level breadth, the role offers exposure to both the technical and commercial sides of building AI products while still working alongside an experienced technical team.

The Role

You will own substantial pieces of customer delivery while contributing to the core platform underneath those deployments. That means understanding messy enterprise systems, defining what should be built, architecting the solution and shipping production agentic systems that solve real business problems.

Over time, the expectation is that you can independently take a high-level customer objective and turn it into a robust technical product.

What You’ll Do

• Build and deploy production AI agents that automate complex enterprise workflows.
• Own technical problems from customer discovery and source-system analysis through architecture, implementation and production.
• Integrate fragmented operational data and software systems into reliable foundations for AI applications.
• Design solutions that can be reused and scaled rather than treating every customer deployment as one-off consulting work.
• Contribute to core platform problems around business context, data representation, agent orchestration and autonomous execution.
• Partner directly with customers to understand requirements, challenge assumptions and identify the data or system changes needed to deliver.
• Take significant ownership with limited hand-holding while collaborating closely with a small engineering team.

What You’ll Bring

• Meaningful hands-on experience building AI agents that were deployed inside a real company or product.
• Strong backend software engineering skills, ideally combined with applied ML or modern AI engineering experience.
• Evidence that you personally owned important parts of systems rather than operating within a narrowly defined silo.
• Experience in an early-stage or fast-moving startup environment where breadth, speed and judgement matter.
• Ability to move comfortably between AI, software, data and product problems as the situation demands.
• Strong communication skills and the ability to work directly with customers on ambiguous business and technical problems.
• High learning velocity, strong technical judgement and a genuine preference for ownership over narrowly scoped execution.

Why Join

• Join at the point where commercial demand is scaling faster than the engineering organisation, creating unusually high ownership and product influence.
• Work on technically difficult agent, data and enterprise-system problems that are tied directly to measurable customer outcomes.
• Build the breadth required to operate like a future technical founder while learning alongside a small, experienced team and sharing in the upside through equity.

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.

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