What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- Bachelor’s degree in Computer Science Engineering Data Science Information Systems Mathematics or related technical field
- 6+ years of production software engineering applied AI engineering ML engineering data-intensive application development backend platform engineering or related technology experience
- Hands-on experience building LLM-powered applications AI agents RAG workflows prompt/context systems evaluation assets AI-assisted workflows or comparable applied AI capabilities
- Experience designing and deploying end-to-end technical capabilities from requirements and prototypes through production implementation monitoring and iteration
- Experience working with product design engineering data analytics quality security privacy and business stakeholders to ship reliable software or AI-enabled product features
- Strong programming skills preferably Python with backend engineering experience across services APIs integrations data flows and production application components
- Hands-on familiarity with LLM application development including prompt engineering RAG pipelines embeddings vector or hybrid retrieval tool calling structured outputs and evaluation methods
- Familiarity with agentic systems and orchestration patterns including multi-step workflows state management memory routing decision logic human-in-the-loop controls and emerging frameworks such as LangGraph LangChain LlamaIndex CrewAI Google ADK OpenAI Agents SDK or comparable tools
- Understanding of cloud and production engineering practices including containers CI/CD monitoring logging deployment security privacy latency cost reliability and operational support
- Ability to work with data and integration patterns including APIs event flows data products enterprise systems searchable knowledge sources and scalable architectures
- Ability to test and evaluate AI-enabled systems including response quality groundedness source attribution task completion hallucination risk latency cost safety and reliability
Nice to have
- Production minded
- Product oriented
- Collaborative
- Reliable
- Analytical
Day to day
- Design, build, integrate, evaluate, and continuously improve production-grade AI-enabled capabilities that support priority AI use cases.
- Take LLM-powered experiences agent workflows RAG patterns tool/API integrations and context-management approaches from concept and prototype into reliable production implementation.
- Partner closely with cross-functional stakeholders to translate workflow needs trusted data sources and enterprise architecture patterns into secure observable and measurable AI capabilities.