Senior Data Scientist

180 - 200 PLN/ godz./ godz.B2BB2B (netto)

18k - 20k PLN18 000 - 20 000 PLN/ mies./ mies.UoPUmowa o pracę (brutto)

SeniorHybryda·WarszawaFull-time·B2B·Umowa o pracę

#470172·Dodano 4 dni temu·0

Źródło: nofluffjobs.com

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Tech Stack / Keywords

PythonObject-oriented programmingEnterprise softwareDesign PatternsMicroservices FastAPI pytest ML LLMBash scriptJenkins Phoenix KubernetesGPUCUDA

Podsumowanie oferty

Develop LLM-based agentic solutions for complex business processes in Investment Banking, as part of a team industrializing applications and integrating generative AI into business workflows. The role focuses on AI agents, data extraction, evaluation and observability, using Python, LangGraph, Google ADK, DSPy and vLLM.

Wymagania

Strong Python skills, with extensive experience in Object-Oriented Programming and enterprise software design patterns.
Proven experience building asynchronous microservices and endpoints using FastAPI.
Hands-on practice with pytest for unit, functional, and integration testing of ML/LLM pipelines.
Use Bash scripts for automation, data handling, and container interaction.
Demonstrated understanding of LLM architecture through a project portfolio.
Experience architecting multi-step, stateful agentic graphs using LangGraph or Google ADK.
Hands-on experience optimizing LLM pipelines programmatically using DSPy.
Practical knowledge of deploying and running high-throughput open-weight models on GPU infrastructure using runtimes such as vLLM.
Familiarity with automated build, test, and release pipelines using Jenkins.
Experience setting up tracing, drift detection, and automated evaluations for generative workflows using tools such as Phoenix Arize.
Commitment to shipping stable, secure code into flagship banking infrastructure where compliance, auditability, and data security are non-negotiable.

Nice to have:

Experience with CUDA and fine-tuning.
Corporate Institutional Banking project experience.

Obowiązki

Design and develop LLM-based agentic solutions to automate complex business processes.
Work with business teams to identify needs and drive the adoption of AI solutions.
Develop data extraction pipelines and implement evaluation and observability frameworks for AI agents.
Deploy and industrialize models (LLMs, GPUs), ensuring quality through testing and DevOps practices.
Take full ownership of driving AI features.
Respect the boundaries and non-deterministic nature of LLMs, communicate constraints clearly, and build robust safety rails.
Communicate technical trade-offs with architects and business users, and mentor peers.
Test ideas and proof of concepts quickly, validate feasibility with data, and avoid technical debt.

Benefity

Sport subscription.
Training budget.
Private healthcare.

Karta sportowa

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