Senior Applied AI Engineer
Summary
Senior engineer builds and deploys LLM-powered agents and RAG systems for clients, owning presales scoping through production deployment with Python and cloud tooling.
About deepsense.ai
deepsense.ai is a 120-person AI/ML consultancy with Anthropic and OpenAI partner credentials. For over a decade we've delivered applied AI projects for companies like J&J, Sky, John Deere, and GLS — spanning LLM applications, agents, MLOps, and data science. We're a people-first organization that believes in deep technical craft and real ownership. Our engineers don't just advise; they build things that run in production.
About the role
The Applied AI Engineer works close to the client across the whole engagement: in the presales conversations understanding what they actually need, drafting the architecture for it, and then delivering it hands-on, super close with the client. You'll move across that work fluidly — one moment shaping the approach in a scoping workshop, the next committing code to their repo and owning a production agent in their environment.
You'll work alongside our own team and the client's, and you'll be equally at home in a discovery workshop and a pull request review.
You must have:
- 4+ years of software engineering experience, with at least 2 years working on AI/ML or LLM-powered systems in production.
- Hands-on production experience with LLMs: prompting, context engineering, agent architectures, tool use, RAG, evaluation.
- Strong Python skills and experience shipping software that real users depend on.
- Demonstrated ability to work directly with clients — you can run a technical scoping call, facilitate a workshop, and earn trust quickly with engineers and non-engineers alike.
- Experience owning deliverables end-to-end in ambiguous environments without close supervision.
- Familiarity with cloud platforms (GCP / AWS / Azure) and containerized deployment (Docker, Kubernetes).
- Experience supporting the sales process as the technical expert — building POCs, scoping, and answering clients' technical questions before a deal is signed.
- Hands-on experience with agentic frameworks: LangGraph, CrewAI, Pydantic AI, or MCP server development.
- Familiarity with LLMOps tooling: LangSmith, Langfuse, W&B, or equivalent.
- Experience building in regulated-industry environments (finance, healthcare, manufacturing).
- Exposure to model evaluation methodologies, or LLM-as-a-judge patterns.