Data Scientist with AI
Role overview
Currently, we are searching for a full-time ML/Data Science Engineer to cooperate with our US client - the company integrates production-grade, governed AI workflows into complex enterprise systems by addressing common friction points like poor integration and lack of domain expertise. The organization combines deep domain knowledge with cutting-edge capabilities to accelerate the deployment of reliable AI products.
Our client’s core mission is to make artificial intelligence work for complex enterprise workflows. Recognizing that most AI initiatives fail due to poor integration, gaps in domain specialization, a lack of governance, and unclear ownership-rather than flawed models—they focus on overcoming these exact friction points.
By combining decades of deep industry domain expertise with cutting-edge product AI capabilities, they accelerate production deployment and embed production-grade, governed AI workflows directly into some of the world’s most intricate enterprise systems. The leadership team consists of proven industry veterans with a track record of delivering groundbreaking AI products, backed by a board of senior executives with deep expertise in global consulting and software engineering.
Responsibilities
- check_circle Translate business needs into data science problems.
- check_circle Design, develop, and evaluate AI-driven solutions for enterprise data, leveraging Large Language Models (LLMs), Agentic AI, prompt engineering, chain design, tool integration, and lightweight agent workflows.
- check_circle Build and optimize solutions using frameworks such as LangChain and LangGraph, alongside traditional machine learning approaches including classification, regression, forecasting, and clustering models.
- check_circle Work with established evaluation: offline metrics, human-in-the-loop reviews, rubric-based GenAI assessments.
- check_circle Utilize Python for model development.
- check_circle Stay up to date with the latest advancements in machine learning and contribute to the adoption of new technologies within the team.
Basic qualifications
- At least 5 years of experience in data engineering.
- At least 3+ years of proven commercial experience designing and implementing scalable AI solutions (Machine Learning, LLM, NLP, Computer Vision, Gen/Agentic AI).
- Strong software engineering fundamentals with a focus on Python.
- Solid MLOps experience across the ML product lifecycle.
- Hands-on experience with LLM frameworks, like LangChain, and LangGraph.
- Have practical experience with MLOps, especially MLflow and CI CD pipelines in GitLab.
- Being able to work until at least 7:00 PM CET.
Preferred qualifications
- A degree in Computer Science, Artificial Intelligence, or a related field.
Benefits
- check_circle 160 – 210 PLN netto per month - B2B
- check_circle gross per month - Employment Contract
Tags & Focus Areas
About Acaisoft
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