Lead GenAI Engineer

You might be our missing piece if you have:

• Strong Python skills and a solid foundation in software engineering-clean architecture, version control, readable code, and good engineering judgment.
• Experience building and shipping production-grade backend applications with FastAPI, Flask, or Django.
• A proven track record of designing and scaling ML and/or GenAI systems in production, from data pipelines and deployment to monitoring, optimization, and evaluation.
• Hands-on experience with GenAI frameworks such as LangChain, LangGraph, ADK, or Haystack.
• A good understanding of commercial LLM APIs (such as OpenAI, Anthropic, and similar providers), including where they shine, where they fall short, and how to work with those trade-offs.
• Strong experience with RAG systems, including embeddings, vector search, document retrieval, chunking strategies, reranking, and context construction.
• Experience designing AI-powered product architectures, including multi-agent systems, tool-using agents, orchestration flows, and inference architectures.
• A solid understanding of agentic workflows-planning, memory, tool use, handoffs, and how to make multi-step systems work reliably in practice.
• Experience defining and implementing evaluation strategies for GenAI systems, covering retrieval quality, answer quality, task success, latency, cost, and hallucination tracking.
• Comfort working with relational and non-relational databases, as well as vector stores, and a good understanding of how data should be modeled and prepared for ML and GenAI use cases.
• Strong hands-on knowledge of Docker and a practical understanding of containerized workflows. DevOps might own the pipelines, but you know how to work well with them.
• Experience building systems that are easy to debug and operate, with solid logging, tracing, monitoring, and observability practices.
• Good judgment around guardrails, privacy, and security when building AI systems that interact with sensitive data, users, or external tools.
• An interest in agentic coding and spec-driven development.
• Experience leading AI/ML initiatives from idea to production.
• A natural tendency to support and mentor others, helping them grow both technically and in the way they approach problems.
• Experience with traditional ML or Computer Vision systems that require model architecture design, feature engineering, and dataset management.

We would be thrilled if you have:

• Experience evaluating and improving agentic systems in real-world settings.
• Familiarity with prompt engineering and context engineering, and a feel for improving consistency, controllability, and tool-calling reliability.
• You have used or implemented MCP servers or clients.
• An eye for research and the ability to turn ideas from papers or experiments into production-ready solutions.
• Comfort working with cloud infrastructure such as AWS, Azure, GCP, or DigitalOcean, along with a good understanding of CI/CD practices.
• Hands-on experience with Databricks or Apache Spark.
• Experience with observability or evaluation tooling built specifically for LLM-based systems.
• The ability to work comfortably across product, engineering, and research, and help connect the dots between them.
• Experience with experiment tracking tools such as MLFlow.
• Experience with core ML frameworks such as PyTorch, Keras, or similar.

Responsibilities

• Understanding client needs and shaping AI solutions that are practical, well-architected, and worth building.
• Designing and delivering agentic RAG systems that are reliable, measurable, and ready for production.
• Leading major AI initiatives from architecture design to delivery.
• Defining evaluation approaches and feedback loops for retrieval, generation, and agent behavior.
• Driving good engineering practices around reproducibility, monitoring, observability, scalability, and operational reliability.
• Helping shape the guardrails, security practices, and engineering standards behind the AI systems we build.
• Mentoring and supporting the AI team through pair programming, code reviews, and hands-on collaboration.
• Keeping a close eye on what is happening in the field and bringing in new ideas when they genuinely make sense.
• Using the latest AI-assisted engineering practices to build thoughtful, high-quality solutions.
• Continuously raising the bar for how we design, build, and deliver AI products.