Lead ML Engineer

Job description

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

- Strong proficiency in English, both spoken and written