About this role
Job Requirements
Mandatory Skills
- 15+ years of experience across software engineering, data engineering, and AI/ML engineering, with 5+ years in senior technology leadership roles
- Strong engineering foundation with hands‑on experience as an individual contributor before transitioning into leadership
- Proven experience building and scaling enterprise AI platforms , including GenAI, ML, and intelligent automation solutions
- Expertise in LLMs, GenAI architectures, agent-based systems, and AI workflow orchestration
- Hands-on understanding of prompt engineering, RAG architectures, vector databases, embeddings, and context management
- Experience implementing AI solutions on cloud-native platforms (GCP, AWS, Azure) , including managed AI services
- Strong proficiency in Python , APIs, and modern engineering stacks; working knowledge of Java/Scala/Go preferred
- Deep understanding of MLOps / LLMOps , CI/CD for AI, model lifecycle management, and observability
- Experience with Responsible AI, AI governance, security, privacy, and compliance , especially in regulated environments
- Proven ability to lead large, distributed, multi-disciplinary teams and drive organizational change
- Strong stakeholder management skills and ability to engage with senior leadership and client executives
Primary Roles and Responsibilities
- Provide strategic and operational leadership for AI Engineering teams delivering enterprise AI, GenAI, and agentic solutions
- Define and execute the AI engineering vision, roadmap, and operating model aligned with business strategy
- Oversee the design and delivery of:
- GenAI platforms and reusable AI capabilities
- AI-powered developer productivity and coding assistant solutions
- Agentic and workflow-driven automation systems
- Ensure scalable, secure, and governed AI architectures , including RAG, orchestration layers, and enterprise context management
- Partner closely with Data Engineering, Cloud Engineering, Product, Security, Legal, and Risk teams
- Drive adoption of MLOps / LLMOps best practices , including CI/CD, evaluation, monitoring, and cost governance
- Establish AI governance frameworks covering responsible AI, model risk, explainability, auditability, and regulatory compliance
- Promote a platform and product mindset , enabling reuse, standardization, and accelerated delivery
- Lead AI modernization initiatives , including migration from experimental AI to production-grade enterprise deployments
- Build and scale high‑performing teams through hiring, mentoring, and leadership development
- Define clear OKRs, KPIs, and success metrics for AI engineering initiatives
- Represent the AI Engineering function in enterprise forums, client engagements, and innovation councils
- Drive collaboration, transparency, and a culture of continuous learning and innovation
Preferred Skills
- Experience with enterprise GenAI platforms (Gemini, OpenAI, Azure OpenAI, Vertex AI, Bedrock, etc.)
Exposure to AI coding assistants, developer copilots, and software modernization using AI
- Knowledge of data mesh or federated AI/data architectures
- Experience working with regulated industries (Healthcare, Life Sciences, BFSI)
- Strong understanding of AI cost management, token optimization, and usage governance
- Demonstrated ability to influence at executive and board levels