deepsense.ai

Senior Applied AI Engineer

19k - 29k PLN19 000 - 29 000 PLN/ mies./ mies.B2BB2B (netto)

SeniorZdalnie·WarszawaFull-time·B2B

#405455·Dodano dwa miesiące temu·8

Źródło: nofluffjobs.com

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Tech Stack / Keywords

Python LLMsRAGCloudDocker KubernetesAICloud platformLangGraphCrewAIMCP serverLangSmithLangfuse

Firma i stanowisko

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

Wymagania

4+ years of software engineering experience, with at least 2 years on AI/ML or LLM-powered systems in production.
Hands-on production experience with LLMs, including prompting, context engineering, agent architectures, tool use, RAG, and evaluation.
Strong Python skills with experience shipping software relied upon by real users.
Ability to work directly with clients, run technical scoping calls, facilitate workshops, and build trust.
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 a technical expert by building POCs and answering technical questions.
Hands-on experience with agentic frameworks such as LangGraph, CrewAI, Pydantic AI, or MCP server development.
Familiarity with LLMOps tooling like 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.

Obowiązki

Partner with Delivery Managers to scope and win AI engagements by running technical discovery, designing solution architectures, and building/demoing POCs.
Act as the primary technical advisor during early post-sales phases.
Work hands-on inside client engagements shipping production code and owning critical AI deployments end to end.
Design and build LLM-powered applications including RAG pipelines, multi-agent systems, MCP integrations, evaluation frameworks, and production inference stacks.
Run technical workshops and architecture reviews; transfer knowledge to client teams.
Contribute to reusable internal assets like solution blueprints, reference implementations, prompt libraries, and evaluation toolkits.
Stay updated on new models, tooling, and patterns in applied AI and integrate that knowledge into client work.

Benefity

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

Karta sportowa

Dofinansowanie szkoleń

Opieka zdrowotna

Firmowa stołówka

Napoje w biurze

Darmowe przekąski

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