Engineering Head AI Platforms

Job Description Engineering Head – AI Platforms & Enterprise Engineering

Experience: 10-15+ years

Location – Mumbai Powai Office

Direct Repartees – 9 to10 Engineers

Reports to – Business Head Product Org CME

Role Summary

Lead the engineering organization responsible for building, delivering and operating an enterprise-grade AI platform. Own engineering execution from architecture handoff through development, quality, deployment and production operations. Drive engineering excellence, scalability, security, reliability and predictable delivery while partnering with Product, Solution Architecture and client engineering and Practice teams.

Key Responsibilities

• Own end-to-end engineering delivery, execution roadmap and release planning.
• Translate architectural designs into scalable, production-ready software platforms.
• Build and lead Backend, AI, Data, Frontend, QA and DevOps engineering teams.
• Establish engineering standards, coding practices, CI/CD and SDLC governance.
• Drive API-first platforms, workflow orchestration, AI integrations and enterprise applications.
• Own engineering estimates, sprint planning, technical risk management and depenedency management.
• Partner with Solution Architects to convert business requirements into production-grade systems.
• Lead production readiness reviews, incident management and continuous improvement.

Engineering Leadership

• Hire, mentor and develop engineering managers and senior engineers.
• Define engineering KPIs covering quality, reliability, velocity and operational SLAs.
• Conduct architecture reviews, design reviews and code governance.
• Build a culture of engineering excellence, automation and ownership.

Platform & Technology

• Lead cloud-native microservices and distributed systems development.
• Oversee API platforms, event-driven architecture and workflow engines.
• Guide integration of third-party AI services and enterprise systems.
• Implement observability, monitoring, logging and telemetry.

Scalability & Reliability

• Drive horizontal scaling, performance tuning and capacity planning.
• Establish load, stress, endurance and failover testing practices.
• Implement resilience patterns including retries, circuit breakers, idempotency and

disaster recovery.

• Ensure secure engineering and compliance best practices.

Required Technical Skills

• Distributed systems, enterprise SaaS and microservices.
• Python plus Java, Go or Node.js.
• Kubernetes, Docker, AWS/Azure/GCP and CI/CD.
• REST/gRPC APIs, Kafka/RabbitMQ, PostgreSQL, Redis and API Gateways.
• Strong understanding of AI platform integration, LLM ecosystems and workflow

orchestration.

• DevOps, observability, security and software quality engineering.

Preferred Domain Experience

• AI platforms, Media & Entertainment, OTT, Video AI, Digital Asset Management,

workflow automation or enterprise SaaS.

Success Measures

• Deliver predictable releases with production-grade quality.
• Build scalable, secure and resilient engineering platforms.
• Improve engineering productivity, platform reliability and customer satisfaction.
• Enable rapid onboarding of new AI capabilities without compromising stability.

AI-Driven Coding & Engineering Practices (Mandatory)

• Demonstrated experience using AI coding assistants such as GitHub Copilot, Cursor,

Windsurf, Claude Code or equivalent.

• Leverage AI to accelerate software design, coding, API development, documentation,

testing, debugging and refactoring while maintaining enterprise-grade quality.

• Review, validate and secure AI-generated code for correctness, performance,

maintainability and security before production deployment.

• Use prompt engineering techniques to improve engineering productivity and software

quality.

• Apply AI-assisted troubleshooting, root-cause analysis and performance optimization

for production systems.

• Follow AI-assisted SDLC best practices while maintaining coding standards, version

control and peer review discipline.