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 results without extensive supervision
- Enterprise thinking: Scales solutions for 8,000+ users
- Business acumen: Understands ROI, cost optimization, time-to-value
- Communication: Executive presentations, cross-team collaboration
- Innovation: Continuous optimization and forward-thinking approach
- Execution excellence: Balances speed with reliability
Pay: ₹510,028.53 - ₹1,872,240.42 per year
Work Location: In person
