AI Engineer, Agentic Systems & Enterprise Data

• AI Engineer, Agentic Systems & Enterprise Data
• Compensation: $200,000–$300,000 base + equity
• Location: New York preferred (3 days/week) or remote across the US

An early-stage AI company building the data and agent infrastructure that powers automation inside complex enterprises.

The company is tackling a fundamental enterprise AI problem: business context is fragmented across ERPs, CRMs, documents, legacy systems and institutional knowledge. Its platform connects those systems, resolves conflicting information and gives AI agents the context required to automate real workflows.

After launching commercially this year, the business has already reached eight-figure ARR with a team of fewer than 10 people. It is venture-backed, profitable and now scaling engineering rapidly.

• The Role
• This is a high-ownership AI engineering role spanning backend engineering, applied AI and customer-facing product development.

You’ll take real business problems from initial scoping through architecture and production, while also contributing reusable components to the core platform.

• What You’ll Do
• Build and deploy production AI agents for enterprise workflows
• Own projects from ambiguous customer problem through production
• Connect fragmented enterprise systems and data sources
• Build data models and ontologies that give agents reliable business context
• Design backend services and infrastructure for agentic applications
• Work directly with customers to understand requirements and constraints
• Turn customer solutions into scalable platform primitives
• Contribute to longer-term R&D around autonomous agent generation

• What You’ll Bring
• Professional experience building AI agents in production
• Strong backend or ML engineering fundamentals
• Startup experience or evidence of operating with similar ownership and pace
• Experience personally building meaningful parts of end-to-end systems
• Comfort working across backend, AI/ML, data and infrastructure
• Ability to turn loosely defined problems into working products
• Strong technical judgment and learning velocity
• Confidence working directly with customers and stakeholders

This isn't a narrow fine-tuning or evals role. The team values engineers with breadth who can move across the stack.

• Tech Stack
• The exact implementation stack is less important than your ability to work broadly across the system. Key areas include:

• Python and Go
• LLMs and agentic systems
• Distributed backend systems and APIs
• Enterprise data integrations
• Docker and Kubernetes
• Terraform / Infrastructure-as-Code
• Cloud-native infrastructure
• CI/CD and observability
• Retrieval, orchestration and task-execution systems

Why Join?

• Huge ownership: With a tiny engineering team, you'll have genuine influence over both customer products and the core platform.
• Real AI in production: The company is deploying agents against commercially valuable problems across revenue, operations, finance and internal knowledge.
• Exceptional trajectory: The business has gone from launch to eight-figure ARR in a matter of months while remaining profitable.
• Founder-level exposure: You'll work directly with customers, founders and senior technical leadership while learning how complex business problems become scalable products.
• Hard technical problems: The team is working toward automated ontology creation, reusable enterprise AI infrastructure and increasingly autonomous agent generation.
• Strong upside: Competitive cash compensation, equity and the opportunity to join before headcount catches up with commercial traction.

• About People In AI
• People In AI connects exceptional AI, machine learning and engineering talent with ambitious companies building the next generation of intelligent products and infrastructure.