Experience: 5–15 years
Role – IC
Reports to – Engineering Head
Location – Mumbai
Role Summary
Build, integrate and deploy enterprise AI applications that leverage multiple third-party AI models within client workflows. The role combines AI engineering, backend engineering and forward deployment responsibilities, ensuring AI solutions are production-ready, deeply integrated with enterprise systems and rapidly adaptable to customer environments.
Key Responsibilities
- Build AI-enabled applications using enterprise-grade software engineering practices.
- Integrate multiple third-party AI services including LLMs, video AI, speech AI and vision AI.
- Develop orchestration logic across AI providers, APIs and enterprise applications.
- Implement AI workflows, prompt chains, agentic workflows and business-rule execution.
- Work directly with client engineering teams to deploy, configure and optimize AI solutions.
- Develop reusable SDKs, APIs and integration accelerators.
- Troubleshoot customer deployments and production issues.
- Collaborate with Solution Architects, Backend Engineers, DevOps and Product teams.
AI Engineering Responsibilities
- Prompt engineering and prompt optimization.
- RAG implementation, embeddings and semantic retrieval.
- Model evaluation, response validation and confidence scoring.
- AI guardrails, safety checks and fallback mechanisms.
- Workflow orchestration using LangGraph, LangChain or equivalent frameworks.
- Model abstraction enabling multiple AI providers.
Forward Deployment Responsibilities
- Deploy AI platforms into client cloud environments.
- Integrate with enterprise APIs, MAM, CMS, DAM, CRM and workflow systems.
- Customize AI workflows for client-specific business processes.
- Support production rollouts, customer pilots and enterprise onboarding.
- Partner with customer architects to resolve integration challenges.
Technical Skills
- Python (mandatory), FastAPI, REST APIs and microservices.
- Experience integrating OpenAI, Gemini, Claude, video AI or speech AI platforms.
- LangChain, LangGraph, CrewAI or similar orchestration frameworks.
- Vector databases (Pinecone, Weaviate, Milvus or equivalent).
- PostgreSQL, Redis and API integrations.
- Docker, Kubernetes and cloud platforms (AWS/Azure/GCP).
- Git, CI/CD and software engineering best practices.
Preferred Experience
- Enterprise SaaS, AI platforms, workflow automation, media technology, OTT or content operations.
- Experience working directly with enterprise customers and solution deployments.
Success Measures
- Rapid delivery of AI-powered business workflows.
- Successful customer deployments and production adoption.
- Reusable, scalable AI integrations across multiple enterprise clients.
- High reliability, low latency and maintainable production code.
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.
