Machine Learning / AI Engineer Lead - Databricks - Eng

Role overview Lead client-facing delivery of production machine learning and AI systems, translating business needs into dependable technical solutions. The work spans conventional machine learning, generative and agent-based applications, data pipelines, and the infrastructure needed to operate them. You’ll also contribute to technical direction and knowledge sharing across the practice.

Responsibilities - Consult with clients and lead AI/ML implementations from problem definition through deployment, explaining proposals, findings, and progress to technical and non-technical stakeholders. - Design, build, and maintain production AI/ML pipelines with CI/CD and MLOps practices that support reliable operation and scaling. - Develop generative and agentic AI solutions, including multi-agent orchestration, evaluation, and retrieval-augmented generation. - Architect machine learning and generative AI systems around customer requirements, and manage supporting deployment infrastructure. - Use distributed computing and data processing technologies to support scalable models and workloads. - Contribute to practice growth through technical assignments, knowledge transfer, and documentation of client engagements and training.

Requirements - At least four years of hands-on experience developing, deploying, and maintaining machine learning, generative AI, or agentic AI systems in production. - At least three years in a client-facing consulting or solutions architecture role focused on technical implementation and delivery. - Strong Python skills for data-intensive and scalable AI/ML applications. - Experience with MLOps lifecycle practices, including model versioning, testing, monitoring, and automated deployment. - Practical experience with infrastructure such as Docker and data pipeline orchestration. - Clear written and verbal communication, and a commitment to continued learning in AI/ML.

Nice to have - Experience with Databricks or other lakehouse platforms, Apache Spark, and pipeline orchestration tools. - Familiarity with RAG, agent evaluation, and multi-agent systems in production.

Benefits and work setup - Remote-first work arrangement. - Certification costs covered for AWS, dbt, Google Cloud, Azure, and Databricks; in-company English lessons. - Birthday leave, an additional annual vacation week, referral bonuses, and monthly benefits marketplace credits. - Childcare reimbursement and an annual team trip.