Requirements

Expected technologies

Python

Docker

Machine Learning

LLMs

GenAI

Data Science

AWS

AI Agents

Optional technologies

Kubernetes

CI/CD

RAG

Operating system

Windows

macOS

Our requirements

• Proven commercial experience (+5 years) designing and implementing scalable AI solutions (Machine Learning, Predictive Modeling, Optimization, LLM, 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.

Optional

• Experience with Big Data technologies like Spark, Hadoop, and Kafka is a plus.
• Leadership and mentoring experience is highly desirable.

Your responsibilities

• Lead the development, implementation, and validation of Machine Learning solutions, ensuring alignment with project objectives.
• Lead the design of AI solution architectures that meet complex business goals and drive innovation.
• Research, test and select the best tools and technologies as well as AI solutions to meet the business requirements.
• Understand clients’ needs and translate business problems into data science problems.
• Architect and oversee the implementation of data pipelines and workflows, setting standards for data quality and security.
• Coordinate with Data Engineering and Software Engineering teams to strategize and oversee the building of AI applications.
• Present findings and recommendations clearly and concisely to stakeholders.
• Prioritize, and control tasks within an Agile/Scrum framework, ensuring timely delivery.