Director - AI Engineering

Summary

GE Vernova's AI Foundry builds the AI that runs and transforms the company: enterprise agents, reusable AI products and process transformations across Power, Wind and Electrification, used by tens of thousands of employees. We are seeking a Director, AI Engineering to lead our India AI Engineering organization, based in Bengaluru, reporting to the Head of AI Engineering.

You will be accountable for the people, technical capability and delivery of the India organization, and for making India a hub that owns enterprise AI capabilities globally rather than executing work designed elsewhere. Your teams will build production AI applications, agents, reusable AI services, retrieval systems, MCP servers and tools, and the cloud-native infrastructure that runs them at enterprise scale.

This role pairs organizational leadership with real technical depth. Expect to spend roughly 25–30% of your time in the engineering work: reviewing architectures and code, leading through critical incidents, and prototyping when a path needs to be proven before the team commits to it.

Roles and Responsibilities:

• Lead approximately 20 engineers, with accountability for hiring, performance, development, retention, engineering culture and delivery.
  

• Grow the organization to the AI Foundry hiring plan while holding a hiring bar consistent with the global team. Personally interview senior candidates. 
  

• Develop engineers into Staff, Senior Staff and Principal technical leaders and into first-line managers. Build succession plans for critical roles. 
  

• Set clear performance expectations and act on them, including addressing underperformance promptly. 
  

• Compete for talent in a crowded market: build GE Vernova's AI engineering presence with universities and the technical community, and own retention of critical talent. 
  

• Measure the team on business outcomes (adoption, realized benefit, cycle time), not output. 
  

• Default to reuse. Build on and extend shared AI products and services instead of bespoke implementations; retire duplicated capability. 
  

• Participate directly in architecture and design reviews for complex AI systems. Review code for new architectural patterns, shared services and high-risk production systems. 
  

• Work alongside teams on critical spikes, difficult integrations and major production incidents. 
  

• Prototype emerging approaches to de-risk them before teams industrialize them. 
  

• Challenge whether GenAI is the right solution, versus deterministic software, workflow automation or traditional ML. 
  

• Keep enough hands-on currency to judge technical quality independently of architects and vendors. 
  

• Define engineering standards for production AI and enforce them through design reviews, code reviews and release gates, without unnecessary process. 
  

• Build evaluation into the development lifecycle: offline and online evaluation of accuracy, groundedness, task completion, latency, reliability and cost, gating every release. 
  

• Require observability, tracing, automated testing, guardrails, human-in-the-loop controls and fallbacks in every production system. 
  

• Drive AI-native engineering: adoption of AI coding assistants and agentic development tools across the team, with measured productivity and quality outcomes. 
  

• Ensure the team complies with Responsible AI, cybersecurity, data privacy, including appropriate handling of controlled technical data. 
  

• Build working connectivity with the US team and the global AI Foundry: shared standards, joint design reviews, rotations and protected overlap hours. 
  

Technical Scope:

• AI applications and agents: LLM applications, agentic workflows, MCP servers and tools, prompt and context engineering, structured outputs and tool calling, multimodal applications. 
  

• Retrieval and data: RAG, semantic search, embeddings and vector databases, enterprise document and [industrial/time-series] data. 
  

• Platform and infrastructure: Python services and APIs, microservices, AWS/Azure/GCP, Docker, Kubernetes, CI/CD and LLMOps. 
  

• Quality, safety and operations: evaluation, observability, testing, security, reliability and cost optimization. 
  

Required Qualifications:

• Masters Degree in Computer Science, Computer or AI Engineering, Electrical Engineering, AI/ML or a related field from an accredited university or college. 
  

• 15+ years of relevant experience in software, platform or AI engineering, including 7+ years of experience in leading engineering teams with responsibility for senior engineers and/or engineering managers. 
  

• 2+ years leading teams that delivered LLM, RAG or agentic applications into production use by real users, beyond pilots and prototypes. 
  

• A hands-on engineering background: has personally designed and built complex production systems, and can still review code and designs at depth. 
  

• Strong Python and modern software practices: CI/CD, automated testing, code review. 
  

• Deep working knowledge of LLM application architecture: RAG, agents, tool calling, structured outputs and evaluation. 
  

• Strong experience with AWS, Azure or GCP, containerized architectures (Docker, Kubernetes) and distributed system design. 
  

• Track record of hiring, developing and retaining strong engineers, including growing engineers into senior technical and management roles. 

• Experience working with stakeholders and teams across time zones, with strong written communication. 
  

Desired Characteristics:

• PhD in a relevant technical field. 
  

• Experience building or scaling an engineering organization in India, ideally one with global ownership mandates. 
  

• Experience in energy, industrial, manufacturing or other regulated industries. 
  

• Recent hands-on use of MCP, agent frameworks, model APIs and AI-assisted development tools. 
  

• Experience with Responsible AI, AI governance and export-controlled environments. 
  

• Experience with fine-tuning, small language models or inference optimization. 
  

• Track record of simplifying architectures, establishing reusable capabilities and reducing technology sprawl. 
  

• Executive communication that stays credible with deeply technical engineers. 
  

• Technical community leadership: open source, publications, conference talks or university partnerships. 

Additional Information