Note: The job is a remote job and is open to candidates in USA. People In AI partners with AI-first startups, scale-ups, and enterprise organizations to connect exceptional engineers with opportunities to build production AI systems and intelligent platforms. The Senior AI Engineer will build and deploy production AI agents, integrate them with enterprise systems and data, and take customer problems from initial scope through production. The role also contributes to agent infrastructure, business ontologies, enterprise data, and autonomous workflow execution.
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, 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
Skills
- 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
- 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
- Startup experience is highly preferred
Benefits
- Equity
- Remote work
- 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.
Company Overview