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.
We will be working together on the following:
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.