Director, Developer AI

Your Opportunity

We are looking for a Director of Developer AI to build an engineering ecosystem using cutting-edge AI/ML and Generative AI technologies. This pivotal role will modernize and design the next-gen Agentic Software Development Lifecycle (SDLC), creating scalable platforms with Agentic AI that integrate intelligence, automation, and security at every phase of software delivery. You will drive the future of developer productivity, embedding AI-native solutions to reduce toil, increasing engineering velocity, and provide the best developer experience in engineering. Additionally, you will apply AI/ML to identify, prioritize, and address security vulnerabilities across codebases, infrastructure, and configurations, ensuring robust and secure solutions.

What you'll do 

• AI-Native SDLC Leadership: Architect a forward-looking, AI-powered and Agentic SDLC aiming to increase developer velocity 10x. Drive continuous improvement through self-healing pipelines, feedback loops powered by telemetry, behavioral signals, and adaptive agents.
• RAG & GenAI Systems for Developers: Build intelligent, low-latency developer tools leveraging Retrieval-Augmented Generation (RAG) pipelines and Agentic AI integrations. Operationalize and integrate semantic search, document chunking, vector indexing (e.g., Pinecone, FAISS, Weaviate), and metadata enrichment to deliver real-time, context-aware knowledge to engineers. Develop and integrate GenAI copilots into IDEs, CLIs, and CI/CD tools for smart code suggestions, error resolution, test generation, and documentation assistance. Use orchestration frameworks (e.g., LangChain, LlamaIndex) to manage complex multi-turn interactions and prompt workflows.
• Developer Experience (DevEx): Design frictionless, AI-augmented workflows across development environments to boost flow state and reduce overhead. Scale internal developer portals with AI-powered onboarding, AI-powered code reviews, documentation retrieval, and contextual guidance.
• AI-Driven Security: Apply LLMs and ML models to detect and fix vulnerabilities across code, configuration, and third-party dependencies. Automate rotation of secrets, insecure patterns, privilege escalation vectors, and misconfigurations using AI-powered SAST/DAST.
• Platform Engineering and Scale: Lead the design and operation of secure, multi-tenant, AI-native developer platforms. Utilize Kubernetes, Argo, Temporal, and Agents to orchestrate scalable, resilient workflows. Ensure enterprise-grade SLAs for developer tooling, LLM inference systems, and evaluation infrastructure. Implement cost-efficient auto-scaling, policy enforcement, and observability across environments.
• ML Ops & Evaluation: Manage model lifecycles using Vertex AI, SageMaker, Azure ML, and Bedrock. Deploy model telemetry, evaluation frameworks, and prompt versioning using tools like Weights & Biases, PromptLayer, and TruLens.
• Hands-On Technical Leadership: Ability to contribute to system design and write production-grade code and POCs in Python, Go, or Java. Rapidly experiment with LLMs (LLAMA, GPT-4, Claude, Gemini) and emerging GenAI and retrieval technologies (e.g., MCP).
• Impact and Metrics: Define KPIs for AI-native tools, measuring productivity lift, adoption, model quality (precision/recall), latency, and risk reduction. Design a robust Developer Productivity Dashboard for data-driven decision making.

This role requires

•  Bachelor or Master's in Computer Science, Engineering, AI/ML, or a related field.
• 10+ years in software engineering, including 5+ in senior technical leadership roles.
  3+ years of direct experience with AI/ML and GenAI system development.
• Strong strategic vision for the use of generative AI within the business context, identifying areas where it can provide competitive advantages or improve operational efficiencies.
• Deep knowledge of generative models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Large Language Models (LLMs) like OpenAI's GPT series, BERT, and Transformer architectures, understanding their architecture, training methods, and applications.
• Strong coding skills in Python, Java, or C#, with a solid grasp of AI-focused libraries such as TensorFlow, PyTorch, Hugging Face Transformers, and other frameworks that support generative AI development.
• Familiarity with cloud platforms that offer specific support for deploying and scaling generative models, such as Google's Vertex AI, Azure's AI tools, and AWS's Machine Learning services.
• Experience in applying generative techniques in practical applications such as content creation, data augmentati