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У роли понятный фокус на внедрении агентного ИИ в продакшн, с высокой ответственностью и актуальными инженерными практиками; стоит учесть широкий набор требований и консалтинговый формат.
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Ответственность за продакшн
ИИ в банкинге
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Текст:
Hirify AI/Оригинал
TL;DR
Senior Data Scientist (AI): Designing and industrializing LLM-based agentic solutions for Investment Banking with an accent on agent architectures, data extraction pipelines, and production model deployment. Focus on building stateful agentic graphs, optimizing and evaluating LLM workflows, and operating secure, observable models on GPU infrastructure.
Location: Warsaw, Poland; hybrid workplace.
Salary: PLN 180–200 per hour.
Company
hirify.global’s Consulting – Polska Team develops and industrializes applications for the Investment Banking division, including generative AI and AI-agent solutions.
What you will do
Design and develop LLM-based agentic solutions that automate complex banking processes.
Work with business teams to identify needs and drive AI adoption.
Build data-extraction pipelines and evaluation, tracing, drift-detection, and observability frameworks for AI agents.
Deploy and industrialize LLMs on GPU infrastructure while maintaining quality through testing and DevOps practices.
Own AI features, build safety rails, communicate LLM constraints, and validate PoCs quickly to avoid technical debt.
Communicate technical trade-offs with architects and business users and mentor peers.
Requirements
Strong Python with extensive Object-Oriented Programming and enterprise software design-pattern experience.
Proven experience building asynchronous microservices and FastAPI endpoints.
Hands-on pytest experience covering unit, functional, and integration testing for ML/LLM pipelines.
Bash scripting experience for automation, data handling, and container interaction.
Understanding of LLM architecture demonstrated through a project portfolio.
Experience architecting multi-step, stateful agentic graphs with LangGraph or Google ADK and optimizing LLM pipelines with DSPy.
Practical experience 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, plus tracing, drift detection, and automated evaluation for generative workflows with tools such as Arize Phoenix.
Ability to ship stable, secure code for banking infrastructure where compliance, auditability, and data security are mandatory.
Nice to have
Experience with CUDA and model fine-tuning.
Culture & Benefits
Permanent employment contract or B2B engagement.
Hybrid work in Warsaw.
Opportunity to build expertise in agentic AI architectures and industrializing LLMs in production.
High-visibility projects within the Investment Banking division.
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