Director - AI Engineer
The role leads the design and scaling of enterprise AI platforms, including GenAI, LLMs, and intelligent automation solutions for KPMG's clients. It requires deep hands‑on expertise in prompt engineering, RAG architectures, and cloud‑native AI services on GCP, AWS, or Azure.
Skills required
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