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

📌 Data Engineer Lead (Mumbai)
🏢 Virtualyyst
📍 Mumbai