Artificial Intelligence Platform Lead

Role Summary

Own end-to-end lifecycle of autonomous AI agents and internal AI accelerators driving multi-crore business impact through automation and productivity transformation.

Build and scale AI agent ecosystem from zero to enterprise production, delivering measurable ROI through agent automation across engineering workflows.

Key Responsibilities

AI Agent Platform Architecture
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
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
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
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
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 Expertise
Must Have
✅ 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 Metrics
Platform Impact
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