AI Platform Lead

Role Summary Lead the design, development, and production deployment of enterprise-grade AI agent platforms serving 8,000+ engineering professionals. Own end-to-end lifecycle of autonomous AI agents and internal AI accelerators driving multi-crore business impact through automation and productivity transformation. MANDATE: Build and scale AI agent ecosystem from zero to enterprise production, delivering measurable ROI through agent automation across engineering workflows. Key ResponsibilitiesAI Agent Platform Architecture (40%) - Architect scalable, production-grade AI agent frameworks for enterprise deployment - Design agent orchestration systems supporting complex multi-agent workflows - Implement enterprise-grade monitoring, tracing, and performance observability - Ensure 99.9% uptime SLA across all production agents - Optimize for cost efficiency and performance at scale Agent Development & Productionization (30%) - Lead development of autonomous AI agents solving high-value business problems - Implement advanced agent capabilities (tool calling, memory, reasoning, planning) - Productionize agent deployments with robust error handling and recovery mechanisms - Optimize inference costs and performance at enterprise scale (1B+ tokens/month) - Establish production readiness standards and deployment practices Internal AI Accelerators (15%) - Create reusable AI tools and accelerators for domain experts - Package complex AI capabilities as low-code/no-code solutions - Drive platform adoption across large engineering user base (8,000+ users) - Measure and demonstrate productivity impact and business value - Build self-service AI capabilities for non-technical users Enterprise Integration & MLOps (10%) - Integrate AI platform with enterprise data lakehouse and analytics layer - Implement comprehensive MLOps pipelines (CI/CD, model registry, versioning) - Establish cost governance and optimization frameworks - Ensure enterprise security, compliance, and data governance standards - Implement monitoring dashboards for cost, performance, and availability Platform Leadership & Strategy (5%) - Define AI agent platform roadmap and technology strategy - Mentor junior AI engineers and establish best practices - Collaborate with cloud vendors and technology partners - Present platform impact and ROI to executive leadership - Drive continuous optimization and innovation Required Technical ExpertiseMUST HAVE (Non-Negotiable) 3+ years production AI agent frameworks (Mosaic AI, LangChain, crewAI, AutoGen, or equivalent) 2+ years enterprise LLM deployments (GPT-4o or equivalent, 1B+ tokens/month scale) Expert Python development (FastAPI, agent orchestration, vector databases) Production MLOps experience (model registry, tracing, monitoring, cost optimization) Enterprise-scale system design (high availability, fault tolerance, observability, cost controls) DOMAIN PREFERRED - Engineering, consulting, or technology services industry experience - Multi-modal AI (vision, document understanding, structured data) - Large-scale data platform integration (lakehouse, real-time analytics) - Databricks ecosystem or Azure cloud platform experience Technical Tools & Stack CORE TECHNOLOGIES: - Python (3.8+, FastAPI, async frameworks) - Databricks ML ecosystem (Mosaic AI, MLflow) - Azure OpenAI or equivalent LLM APIs - Vector databases (Pinecone, Weaviate, Qdrant, or Databricks Vector Search) AGENT FRAMEWORKS: - LangChain / LlamaIndex - crewAI / AutoGen - Custom orchestration frameworks - RAG (Retrieval Augmented Generation) systems MLOPS STACK: - MLflow (model registry, experiment tracking) - Databricks Workflows / Apache Airflow - Monitoring: Weights & Biases, Prometheus/Grafana - CI/CD: GitHub Actions, GitLab CI, or Jenkins CLOUD PLATFORMS: - Azure (Databricks, Azure OpenAI, Fabric, Entra ID) - AWS or GCP (equivalent enterprise experience acceptable) - Containerization: Docker, Kubernetes basics OPTIONAL BUT VALUABLE: - Prompt engineering / few-shot learning - Embeddings and semantic search - Token optimization techniques - Cost forecasting and budget management Business Impact & Success MetricsPlatform Impact (Owned by this role) - Revenue Productivity: Multi-crore annual value through automation - Engineering Efficiency: 20%+ productivity improvement across user base - Cost Discipline: Enterprise-scale inference cost optimization - Strategic Advantage: First-mover AI capability in domain Leadership & Organizational Fit REPORTING STRUCTURE: - Direct report to Chief Digital Officer (C-level access) - Individual contributor initially - Team lead expansion (3-5 engineers by Year 2) SPAN OF INFLUENCE: - Cross-functional leadership across engineering, data, and BI teams - Strategic technology partner relationships - Vendor and consultant coordination - Executive steering committee participation CULTURAL FIT REQUIRED: - Ownership mindset: Delivers .