Artificial Intelligence Engineer

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.