AI Platform Lead

Virtualyyst; Mumbai; On-site; INR 4,000,000 - 8,000,000; Full time

Job summary

Virtualyyst is seeking a senior leader to drive the design, development, and production deployment of an enterprise-grade AI agent platform. You will own the end-to-end lifecycle of autonomous AI agents and internal accelerators, delivering measurable ROI through automation across engineering workflows.

Mandate: build and scale the agent ecosystem, achieve 99.9% uptime, and optimize costs at scale for 8,000+ engineering users in a large enterprise environment.

Qualifications

• 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).
• 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).

Responsibilities

• Architect scalable, production-grade AI agent frameworks for enterprise deployment.
• Design agent orchestration systems for complex multi-agent workflows.
• Implement enterprise-grade monitoring, tracing, and observability.
• Ensure 99.9% uptime across all production agents.
• Lead development of autonomous AI agents solving high-value business problems.
• Productionize deployments with robust error handling and recovery.
• Optimize inference costs and performance at enterprise scale.
• Establish production readiness standards and deployment practices.
• Create reusable AI tools and accelerators for domain experts.
• Drive platform adoption across 8,000+ users.
• Measure productivity impact and business value.
• Build self-service AI capabilities for non-technical users.
• Integrate AI platform with data lakehouse and analytics layer.
• Establish MLOps pipelines (CI/CD, model registry, versioning).
• Ensure security, compliance, and data governance.

Skills

AI agent frameworks

LLM deployments

Python development

MLOps

system design HA/observability

Tools

FastAPI

Databricks ML

Azure OpenAI

Vector databases

Job description

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 Responsibilities

AI 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 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 & 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 (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 .