Senior Applied AI Engineer

Lodzplus; Polska; Hybrid; PLN 180,000 - 260,000; Full time

Job summary

deepsense.ai is seeking an Applied AI Engineer who works closely with clients from presales through delivery, drafting architectures and delivering production-grade AI solutions. You will move between scoping workshops and writing production code, contributing to end-to-end deployments of LLM-powered applications.

You’ll collaborate with our team and client engineers, design RAG pipelines, multi-agent systems, MCP integrations, and evaluation toolkits, and help build reusable assets that extend

Qualifications

• Experience guiding AI engagements from discovery to production deployment.
• Ability to shape client architecture and deliver working AI solutions.
• Strong collaboration with cross-functional teams and clients.

Responsibilities

• 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.

Skills

Technical architecture

LLM integration

Production code delivery

MLOps

Client collaboration

Tools

RAG pipelines

Multi-agent systems

MCP integrations

Evaluation toolkits

Job description

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.

Zakres obowiązków

• 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.