Lead ML Operations Engineer

Job Summary

Leads product ownership of enterprise AI and Generative AI solutions built on ChatGPT and LLM platforms, driving adoption, governance, and business value across the AI Champions ecosystem. Acts as the primary technical and functional owner for ChatGPT-based solutions, managing the lifecycle of AI use cases and ensuring alignment with enterprise policies and standards.

Works closely with AI Champions, business stakeholders, and engineering teams to enable, support, and scale GenAI solutions. In addition to ChatGPT ownership, contributes to broader AI initiatives involving AWS and Snowflake-based architectures, ensuring integration with enterprise data platforms and scalable solution design.

Key Areas of Responsibility

• ChatGPT Product Ownership
  Own roadmap, prioritization, and lifecycle of ChatGPT-based solutions and GenAI use cases across business functions.

• AI Champions Enablement & Support
  Act as the central contact for AI Champions, guiding use case development, onboarding, troubleshooting, and adoption.

• Use Case Identification & Scaling
  Partner with business teams to identify high-impact AI use cases and drive scaling across functions.

• LLM & GenAI Solution Guidance
  Provide expertise in LLM-based solutions including prompt engineering, RAG, conversational AI, and agent-based workflows.

• Agent / Workflow Lifecycle Management
  Oversee design, testing, deployment, and monitoring of ChatGPT-powered agents, assistants, and workflows.

• Multi-Agent Ecosystem Enablement
  Guide design and implementation of multi-agent architectures (planning, retrieval, summarization, orchestration) for complex workflows.

• Governance, Compliance & Risk
  Ensure adherence to enterprise AI governance (PIA/BIA, security, privacy, DLP policies) and responsible AI practices.

• Issue Management & Operational Support
  Manage and resolve platform, access, usage, and solution-related issues to ensure smooth operations for AI Champions.

• AI Catalogue & Reporting
  Track AI use cases, adoption, performance, and business impact across ChatGPT-based initiatives.

• AWS & Snowflake Project Support (Secondary Responsibility)
  Support AI/ML and GenAI projects leveraging AWS and Snowflake, contributing to solution design, data integration, and platform alignment.

• Stakeholder Management & Communication
  Collaborate across business, technical, and governance teams and present updates, insights, and recommendations to leadership.

• AI Adoption & Community Building
  Drive enterprise adoption through training, best practices, and active engagement with the AI Champions community.

Critical Experiences

• Experience
  Minimum 5+ years of experience in Machine Learning, Data Science, AI Engineering, or related roles, with exposure to GenAI and LLM-based solutions.

• Education & Certifications
  Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or related field. Relevant AI/ML or cloud certifications are desirable.

• ML & LLM Expertise
  Strong understanding of ML and LLM concepts including prompt engineering, RAG, agent-based systems, and conversational AI.

• ChatGPT / GenAI Platforms
  Hands-on experience or strong exposure to ChatGPT (enterprise versions), LLM APIs, or similar GenAI platforms.

• Product Ownership / Delivery Experience
  Experience managing product backlog, prioritization, roadmap planning, and stakeholder engagement.

• AI Champions / Federated AI Model Exposure
  Experience working in federated AI environments or enabling business users to build and scale AI solutions.

• AWS & Snowflake Exposure (Supporting Role)
  Working knowledge or project exposure to AWS and Snowflake for data integration, ML workflows, and enterprise data platforms.

• Governance & Compliance
  Understanding of AI governance frameworks (PIA/BIA, privacy, security, DLP).

• Agent / Workflow Lifecycle Understanding
  Experience or familiarity with designing, deploying, and monitoring AI assistants, workflows, or agent-based systems.

• Technical Skills
  Proficiency in Python and SQL with the ability to evaluate AI outputs, debug issues, and guide improvements.

• Leadership & Communication
  Strong stakeholder management, cross-functional collaboration, and ability to influence adoption at scale.