Senior AI Engineer

People In AI; New York (NY); Hybrid; USD 200,000 - 300,000; Full time

Job summary

People In AI, an early-stage applied AI company, is hiring an engineer to build production AI agents and connect enterprise workflows. Located in New York, NY with hybrid 3 days in office.

You will own problems from customer conversation through architecture and deployment, and work with founders to advance agent infrastructure and enterprise data platforms while delivering real business impact.

Qualifications

• Must have shipped production AI agents in a company.
• End-to-end ownership of systems used by real users.
• Strong backend engineering fundamentals.

Responsibilities

• Build and own production AI agents used in real enterprise workflows.
• Take customer problems from initial scope through architecture, implementation, deployment, and iteration.
• Integrate agents with enterprise systems, APIs, and data.
• Work with fragmented data sources to create usable business context.
• Design reliable backend systems supporting production agent execution.
• Diagnose agent failures and production issues.
• Define success metrics and improve systems based on outcomes.
• Contribute to shared platform capabilities across customers.
• Collaborate with founders and leadership while owning significant work.

Skills

Production AI agents

Backend software

LLMs & agents

APIs

Orchestration

Tools

APIs

LLMs tooling

Job description

Compensation: $200,000 - $300,000 base + equity

Location: New York, NY (Hybrid - 3 days in office)

Join an early-stage applied AI company building the infrastructure and agent layer required to automate complex enterprise knowledge work. The company is already seeing exceptional commercial traction and is now expanding a very small engineering team behind that growth.

This is a highly autonomous role for an engineer who has already built production AI agents and wants to work across backend engineering, AI systems, data, product, and customer-facing

The Mission

The company is building AI systems that can understand how businesses actually operate.

That means connecting fragmented enterprise systems, working through messy operational data, creating a unified business context layer, and deploying agents directly into real workflows.

The long-term goal is to turn traditionally manual data integration, application development, and workflow automation into reusable AI-native platform capabilities rather than one-off consulting projects.

The Role

You will take ambiguous business problems from initial customer conversation through architecture and production deployment. You'll work directly with customers to understand their objectives, inspect source systems and data, determine what is technically possible, and build a working V1. At the same time, you'll contribute to the core platform and help solve harder technical problems around agent infrastructure, enterprise data, ontology generation, and autonomous workflow execution.

What You'll Do

• Build and own production AI agents used in real enterprise workflows.
• Take customer problems from initial scope through architecture, implementation, deployment, and iteration.
• Integrate agents with enterprise systems, APIs, operational data, and existing workflows.
• Work with fragmented and overlapping data sources to create usable business context.
• Design reliable backend systems supporting production agent execution.
• Diagnose agent failures, reliability issues, and unexpected production behaviour.
• Define how success is measured and improve systems based on real customer outcomes.
• Contribute to shared platform capabilities that can be reused across customers.
• Help solve R&D problems around business ontologies, agent generation, and AI-native enterprise infrastructure.
• Work directly with founders and technical leadership while taking significant ownership over your work.

What You'll Bring

• Meaningful experience personally building production AI agents inside a company.
• Clear end-to-end ownership of systems that reached real users or business workflows.
• Strong backend software engineering fundamentals.
• Experience working with LLMs, agents, orchestration, tools, APIs, or AI-driven workflows.
• Ability to discuss architecture decisions, failure modes, debugging, reliability, and production tradeoffs in depth.
• Comfort working across backend engineering, AI, data, infrastructure, and product.
• Strong technical judgement and the ability to operate independently in ambiguous environments.
• Startup experience is highly preferred.
• Customer-facing or forward-deployed engineering experience is valuable.
• ML experience is useful, but narrow specialization in areas such as fine-tuning or evals alone is not the target.

Why Join?

• Join at a rare stage where commercial traction has arrived before the organization has meaningfully scaled.
• Take significantly more technical and customer ownership than you would in a traditional engineering role.
• Work directly with founders on hard AI and enterprise infrastructure problems.
• Build systems that solve measurable business problems rather than isolated demos or proofs of concept.
• Contribute to both customer-facing AI deployments and the underlying platform powering them.
• Gain unusually broad exposure across technology, customers, product, and commercial decision-making.

About People In AI

We partner with AI-first startups, scale-ups, and enterprise organizations to connect exceptional engineers with opportunities to build production AI systems, intelligent platforms, and the next generation of enterprise AI.