Lead ML Engineer

• Strong Python proficiency across the core ML ecosystem, with a proven track record of building and deploying complex ML systems in production
• Deep expertise in LLMOps, AgentOps, and experiment tracking tools such as MLflow, Langfuse, LangSmith, and Weights & Biases
• Proven experience architecting and deploying cloud-based AI/ML solutions on AWS, Azure, or GCP, with strong knowledge of CI/CD/CT practices and workflow orchestration tools
• Advanced knowledge of agentic AI frameworks such as LangGraph or CrewAI, with experience designing multi-agent systems for production environments
• Demonstrated experience leading or mentoring ML Engineering teams, driving technical vision, and establishing engineering best practices
• Strong stakeholder management skills, with the ability to shape ML strategy and communicate complex technical decisions to clients and executive audiences
• Master’s degree in Computer Science or a related field
• Strong proficiency in English, both spoken and written

ABOUT THE ROLE

In this role, you will set the technical direction for ML engineering and oversee the delivery of production-grade AI systems, from LLMOps infrastructure and agentic pipelines to scalable multimodal solutions, within SoftServe’s AI and Data Science Center of Excellence. You’ll bridge applied research and real-world impact, leading cross-functional teams and shaping ML strategy for world-leading clients

,[Define and drive technical standards and architecture for end-to-end ML systems, ensuring scalability, reliability, and alignment with business objectives, Oversee the design and delivery of production-ready LLMOps pipelines using MLflow, LangSmith, Langfuse, or Weights & Biases, establishing observability, reproducibility, and quality standards across teams, Lead collaboration with Data Scientists, Engineers, and clients, translating complex business requirements into robust ML solutions for NLP, RAG systems, and multimodal models, Advance agentic AI capabilities by architecting and overseeing multi-agent systems using LangGraph or CrewAI, driving autonomy and real-world problem-solving at production scale, Establish and continuously improve ML engineering processes, from CI/CD/CT pipeline governance and cloud infrastructure on AWS, Azure, or GCP to monitoring and security best practices, Mentor and guide ML Engineers, embedding engineering excellence, MLOps best practices, and a culture of continuous learning across the team, Contribute to technical strategy and thought leadership, supporting client engagements and innovation initiatives within the CoE] Requirements: Python, MLflow, AI, AWS, Azure, GCP, Stakeholder management, Degree Additionally: Sport subscription, Training budget, Private healthcare, International projects, Flat structure, Small teams, Free coffee, Canteen, Bike parking, Free snacks, Free parking, In-house trainings, Modern office, No dress code.