Design, build, and deploy autonomous agentic workflows for internal operations as well as customer-facing products
Integrate LLMs with our systems: APIs, databases, document stores, internal tools, and product interfaces
Build autonomous agents that plan, retrieve information, call tools, execute workflows, and return structured, auditable outputs
Prototype fast while keeping reliability, observability, security, latency, and cost in view
Develop reusable components for orchestration, prompting, tool calling, memory, retrieval, evaluation, and monitoring
Translate ambiguous workflow needs into working systems alongside product and domain experts
Measure model behavior: failure modes, hallucination risk, latency, cost, and the quality tradeoffs between them
Contribute to internal standards for safe, useful, and measurable deployment of agentic systems

Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field
Proven experience (3-6 years) building software solutions and/or working with AI and Machine Learning technologies
Strong programming ability in Python
Solid grounding in machine learning: how models are trained and evaluated, how embeddings behave, where models break, and how to measure quality with statistical rigor
Hands-on experience building with LLM APIs such as OpenAI, Anthropic, Gemini, or open-source models
Fluency with agentic patterns: tool and function calling, retrieval-augmented generation, memory, planning, and orchestration
Ability to connect AI systems to real tools: APIs, databases, web services, document stores, and internal applications
Sound product judgment: you can tell a flashy demo from something that solves a real business problem
Clear communication across technical and non-technical teams

AI Solution Engineer

Key skills