Artificial Intelligence Engineering Lead

Tata Consulting Engineers; Navi Mumbai; On-site; INR 3,500,000 - 5,500,000; Full time

Job summary

Tata Consulting Engineers is seeking a seasoned AI Platform Engineer to design and lead production-grade AI agent platforms in a large enterprise environment. You will architect orchestration for multi-agent workflows, implement robust monitoring and observability, and drive cost-efficient, scalable deployments.

The role focuses on delivering high-availability AI agents, implementing advanced capabilities, and ensuring enterprise security and governance across platform deployments.

Qualifications

• 3+ years production AI agent frameworks experience (Mosaic AI, LangChain, crewAI, AutoGen, or equivalent).
• 2+ years enterprise LLM deployments (GPT-4o or equivalent, 1B+ tokens/month).

Responsibilities

• 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.
• 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.

Skills

AI agent frameworks

LLM deployments

Tools

Mosaic AI

LangChain

crewAI

AutoGen

Job description

Engineering Graduate / Post Graduate is a must

Key Responsibilities

• 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
• 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
• 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) (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:

• Databricks ML ecosystem (Mosaic AI, MLflow)
• Vector databases (Pinecone, Weaviate, Qdrant, or Databricks Vector Search)

AGENT FRAMEWORKS:

• crewAI / AutoGen
• Custom orchestration frameworks
• RAG (Retrieval Augmented Generation) systems

MLOPS STACK:

• Monitoring: Weights & Biases, Prometheus/Grafana
• CI/CD: GitHub Actions, GitLab CI, or Jenkins

CLOUD PLATFORMS:

• AWS or GCP (equivalent enterprise experience acceptable)

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