Lead Data Scientist / Machine Learning Engineer

Quick Facts

• Role: Lead Data Scientist / Machine Learning Engineer

• Focus: Lead ML/AI solutions end-to-end—from architecture and modeling to production delivery

• Key themes: optimization, recommendation systems, and LLM-powered applications

Description

You will lead the development, implementation, and validation of scalable Machine Learning and AI solutions, aligned with business objectives and delivery timelines. The role includes designing AI architectures, translating client business problems into data science tasks, and overseeing data pipelines with standards for data quality and security. You will also research and select the best tools and technologies—including LLM approaches such as RAG and semantic search—and present results clearly to stakeholders.

Responsibilities

• Lead development, implementation, and validation of Machine Learning solutions

• Lead design of AI solution architectures for complex business goals

• Research, test, and select tools/technologies and AI solutions to meet business requirements

• Understand client needs and translate business problems into data science problems

• Architect and oversee data pipelines and workflows, ensuring data quality and security

• Coordinate with Data Engineering and Software Engineering teams to build AI applications

• Present findings and recommendations clearly to stakeholders

• Prioritize and control tasks within Agile/Scrum to ensure timely delivery

Requirements

• 7+ years of commercial experience in designing and implementing scalable AI solutions (ML, predictive modeling, optimization, NLP, computer vision, GenAI, LLMs, deep learning)

• Proficiency in developing ML algorithms from scratch to production deployment

• Strong Python skills (clean code, OOP) and extensive knowledge of ML libraries (Scikit-Learn, PyTorch, TensorFlow)

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

• Knowledge of LLM techniques: AI agents, semantic search, prompt engineering, multimodal embeddings, RAG

• C1-level English proficiency

• Excellent communication skills and consulting/client-facing experience

• Experience with SQL and NoSQL databases (MongoDB, Snowflake, Databricks)

• Good knowledge of CI/CD principles using GitHub and GitHub Actions

• Familiarity with MLOps practices, Kubernetes, and Docker

• Master’s or Ph.D. in Computer Science, Data Science, Mathematics, Physics, or a related field

• Big Data experience with Spark, Hadoop, and Kafka is a plus

Benefits

• Flexible work arrangements (remote or from modern offices/coworking spaces)

• Career growth, knowledge-sharing initiatives, language classes, and sponsored training/conferences (including Databricks and Anthropic learning resources)

• Team-building events and integration budget

• Medical and sports packages, eye care, and well-being support (including psychotherapy and coaching)

• Work equipment provided (laptop and necessary devices)

• Support for personal brand building via conferences, blog writing, and meetups

• Dedicated-buddy onboarding and an autonomous, supportive team culture