Senior Applied AI Engineer @ deepsense.ai Sp. z o.o.

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:

• 6+ 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).

You may 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.

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.

,[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, where the engagement calls for it, 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 in ways that 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.] Requirements: Python, LLMs, RAG, Cloud, Docker, Kubernetes, AI, Cloud platform, LangGraph, CrewAI, MCP server , LangSmith, Langfuse Additionally: Sport subscription, Training budget, Private healthcare, Lunch card, Small teams, International projects, Free coffee, Canteen, Free snacks, Free beverages, Free lunch, In-house trainings, Modern office, No dress code, Free breakfast, In-house hack days.