Senior Data Scientist

200 - 220 PLN/ godz./ godz.B2BB2B (netto)

20k - 22k PLN20 000 - 22 000 PLN/ mies./ mies.UoPUmowa o pracę (brutto)

SeniorHybryda·WarszawaFull-time·B2B·Umowa o pracę

#479441·Dodano 2 dni temu·3

Źródło: nofluffjobs.com

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Tech Stack / Keywords

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

Podsumowanie oferty

You will design and develop LLM-based agentic solutions to automate complex business processes for the Investment Banking division, including use cases in document analysis, credit and finance. The role focuses on industrializing generative AI solutions and AI agents using Python, FastAPI, LangGraph or Google ADK, and DSPy.

Wymagania

Strong Python skills and extensive experience with object-oriented programming and enterprise software design patterns.
Proven experience building asynchronous microservices and endpoints using FastAPI.
Hands-on experience with pytest for unit, functional and integration testing of ML/LLM pipelines.
Experience writing Bash scripts for automation, data handling and container interaction.
Understanding of LLM architecture demonstrated 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; experience with CUDA and fine-tuning is a strong plus.
Hands-on 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 Arize Phoenix.
Dedication to shipping stable, secure code into flagship banking infrastructure, where compliance, auditability and data security are non-negotiable.

Nice to have:

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 ownership of driving AI features.
Respect the boundaries and non-deterministic nature of LLMs, communicate constraints clearly, and build 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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Margo

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