Director - AI Engineer

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