Artificial Intelligence Engineering Lead

Qualification

Engineering Graduate / Post Graduate is a must

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 (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 & Stac

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 (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