Data Scientist / ML Engineer

Addepto is a leading AI consulting and data engineering company that builds scalable, ROI-focused AI solutions for large enterprises and startups, including Rolls Royce, Continental, Porsche, ABB, and WGU. The company focuses exclusively on Artificial Intelligence and Big Data, developing products like ContextClue and contributing open-source solutions. Addepto is part of KMS Technology, a US-based global technology group, combining AI specialization with enterprise-scale delivery capabilities.

• At least 3+ years of proven commercial experience designing and implementing scalable AI solutions including Machine Learning, Predictive Modeling, Optimization, NLP, Computer Vision, and GenAI.
• Proficiency in developing ML algorithms from scratch to production deployment.
• Strong programming skills in Python with 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 client interaction.
• Experience with 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 related field.
• Experience with Big Data technologies like Spark, Hadoop, and Kafka is a plus.
• Practical experience with frontend frameworks (React, Node.js, Nest.js, Typescript) is nice to have.

• Develop, implement, and validate Machine Learning solutions tailored to specific project requirements.
• Understand clients' needs and translate business problems into data science problems.
• Design scalable AI solution architectures.
• Develop data pipelines and workflows, ensuring the highest standards of data quality and security.
• Collaborate with Data Engineering and Software Engineering teams to build and deploy AI applications.
• Present findings and recommendations clearly and concisely to stakeholders.
• Effectively plan tasks within an Agile/Scrum framework.