Senior Applied AI Engineer

deepsense.ai is a 120-person AI/ML consultancy with Anthropic and OpenAI partner credentials. For over a decade they've delivered applied AI projects for companies like J&J, Sky, John Deere, and GLS — spanning LLM applications, agents, MLOps, and data science.

• 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 — running technical scoping calls, facilitating workshops, and earning trust with engineers and non-engineers.
• Experience owning deliverables end-to-end in ambiguous environments without close supervision.
• Familiarity with cloud platforms (GCP / AWS / Azure) and containerized deployment (Docker, Kubernetes).

Nice to have:

• 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.

• Partner with Delivery Managers to scope and win AI engagements — run technical discovery, design solution architectures, build and demo POCs that help win the work.
• Act as the primary technical advisor during early post-sales phases, shaping how clients build on top of LLMs and agentic systems.
• Work hands-on inside client engagements — in their systems and close to their team — shipping production code and owning critical AI deployments end to end.
• Design and build LLM-powered applications: RAG pipelines, multi-agent systems, MCP integrations, evaluation frameworks, and production inference stacks.
• Run technical workshops and architecture reviews; transfer knowledge to build lasting capability in client teams.
• Contribute to reusable internal assets: solution blueprints, reference implementations, prompt libraries, evaluation toolkits.
• Stay at the leading edge of the applied AI space — new models, tooling, and patterns — and bring that knowledge into client work.