Must have

• Machine Learning

• Data science

• AI

• Python

• Cloud

• GenAI

• LLM

• English (C1)

Nice to have

• TensorFlow

• Kubernetes

• MLflow

• CI/CD

• AWS

• PyTorch

• Docker

• RAG

• LangChain

• LlamaIndex

• OpenAI

• Huggingface

• Azure

• MLOps

• Computer vision

• Polish

Requirements description

🎯 What you’ll need to succeed in this role:

• At least 3+ years of proven commercial experience designing and implementing scalable AI solutions ( Machine Learning, Predictive Modeling, Optimization, NLP, Computer Vision, GenAI).

• Proficiency in developing ML algorithms from scratch to production deployment.

• Strong programming skills in Python: writing clean code, OOP design, extensive knowledge of ML libraries (Scikit-Learn, PyTorch / Tensorflow).

• Proven expertise in deploying solutions in cloud environments ( AWS or Azure).

• Knowledge in applying LLMs(AI agents, semantic search, prompt engineering, multimodal embeddings, RAG).

• High proficiency in English (C1 level).

• Excellent communication skills and consulting experience with direct interaction with clients.

• Working experience in SQL and NoSQL databases (MongoDB, Snowflake, Databricks).

• Good knowledge of CI/ CD principles (GitHub and GitHub Actions).

• Familiarity with MLOps practices, Kubernetes and Docker.

• Bachelor’s or Master's degree in Computer Science, Data Science, Mathematics, Physics, or a related field.

• Experience with Big Data technologies like Spark, Hadoop, and Kafka is a plus.

• Practical experience with frontend frameworks (e.g., React, Node.js, Nest.js, Typescript) is nice to have.

Offer description

Addepto is a leading AI consulting and data engineering company that builds scalable, ROI-focused AI solutions for some of the world’s largest enterprises and pioneering startups, including Rolls Royce, Continental, Porsche, ABB, and WGU. With our exclusive focus on Artificial Intelligence and Big Data, we help organizations unlock the full potential of their data through systems designed for measurable business impact and long-term growth.

Beyond client projects, we have developed our own product offerings born from real-life client insights and challenges. We are also actively releasing open-source solutions to the community, transforming practical experience into tools that benefit the broader AI ecosystem. This commitment to scalable innovation, proven ROI delivery, and knowledge sharing has earned us recognition by Forbes as one of the top 10 AI consulting companies worldwide.

As a Data Scientist / ML Engineer, you will have the exciting opportunity to work with a team of technology experts on challenging projects across various industries, leveraging cutting-edge technologies. Here are some of the projects we are seeking talented individuals to join:

• An AI-powered platform for industrial engineering, where you will build a backend system that utilizes LLMs to transform complex technical documents into a structured knowledge graph. This work enables our clients to leverage advanced features such as semantic search, automated documentation, and smarter knowledge management.
• Implementation, deployment, and ongoing development of an automotive data solution, this includes the processing and analysis of various document types, with a strong focus on digital file formats. The core functionality involves extracting key information and identifying complex relationships between disparate data sources. The solution also features seamless integration with the client’s existing infrastructure.
• Development of AI solutions to support software engineering, this involves building a system for advanced analysis of source code repositories, seeking relationships based on files and documentation. It also includes creating a system of grammatical rules for unknown programming languages and automatically generating training content based on these rules.

Your responsibilities

1. Develop, implement, and validate Machine Learning solutions tailored to specific project requirements.
2. Understand clients' needs and translate business problems into data science problems.
3. Design scalable AI solution architectures.
4. Develop data pipelines and workflows, ensuring the highest standards of data quality and security.
5. Collaborate with Data Engineering and Software Engineering teams to build and deploy AI applications.
6. Present findings and recommendations clearly and concisely to stakeholders.

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