Lead ML Engineer

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.

Responsibilities

• 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

• 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
• Upper-intermediate or higher proficiency in English, both spoken and written