Lead ML Operations Engineer

Signify seeks a Lead ML Operations Engineer in Bangalore, Karnātaka, India, to lead product ownership of enterprise AI and generative AI solutions built on Copilot and LLM platforms. The role focuses on adoption, governance and business value across its AI Champions ecosystem, with secondary support for AI/ML projects using AWS and Snowflake. The position is full-time, at 40 hours per week.

Responsibilities

• Own the roadmap, backlog priorities and lifecycle of Copilot solutions and GenAI use cases. Serve as the main technical and functional contact for AI Champions, supporting onboarding, development, troubleshooting and adoption.
• Work with business teams to identify valuable use cases and scale them across functions. Guide LLM solutions involving prompt engineering, retrieval-augmented generation, conversational AI and multi-agent workflows.
• Oversee the development, testing, publishing and monitoring of Copilot agents. Resolve agent, licensing and platform issues; track use cases, adoption, performance and business impact.
• Apply enterprise AI governance, including PIA/BIA processes, security, privacy, data loss prevention and responsible AI practices. Collaborate with business, engineering and governance teams, report to senior leadership, and promote adoption through training and workshops.
• As a secondary responsibility, support AWS- and Snowflake-based AI/ML and GenAI projects with solution design, data integration and alignment with enterprise platforms.

Qualifications

• Minimum 5+ years of experience in machine learning, data science, AI engineering or related roles, with exposure to GenAI and LLM solutions. A bachelor’s or master’s degree in computer science, data science, AI or a related field is required; relevant AI/ML or cloud certifications are desirable.
• Strong ML and LLM knowledge; hands-on experience or strong exposure to Microsoft Copilot, Copilot Studio or a similar enterprise GenAI platform; AI or digital product ownership experience; and experience in federated AI environments or enabling business users to scale AI solutions.
• Working knowledge or project exposure to AWS and Snowflake, familiarity with agent lifecycles and AI governance, proficiency in Python and SQL, and strong stakeholder management and communication skills.