Generative AI Platform Engineer

We're looking for a hands-on Software Engineer to build enterprise-grade Generative AI, Data Science, and AI Platform capabilities within our strategic AI ecosystem (MALTS platform). You'll design and deliver reusable GenAI services, frameworks, APIs, and platform components that power AI model development, deployment, inferencing, and governance across Consumer, Banking, and Wealth organizations — accelerating AI adoption enterprise-wide.

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Job Location :Chennai/Pune

Mode of Work: Hybrid – 2 Days Work From Office per Week

Experience:6 - 12 years

What You'll Do

• Build AI-powered applications, agentic workflows, RAG solutions, and MCP-enabled services
• Develop scalable APIs and microservices supporting the full AI/ML lifecycle
• Build frameworks for model development, fine-tuning, deployment, inferencing, monitoring, and observability
• Implement event-driven/streaming solutions using Kafka and distributed processing platforms
• Contribute to CI/CD pipelines, automation, and testing strategies
• Collaborate with architects, data scientists, and product owners on design, code reviews, and sprint planning
• Ensure solutions meet enterprise standards for security, scalability, governance, and resiliency.

What You'll Need

• Bachelor's/Master's in Computer Science, Engineering, Data Science, or related field
• 6+ years of software engineering experience with strong Python expertise
• Experience building AI/ML, GenAI, or data engineering applications in enterprise environments
• Hands-on experience with REST APIs/microservices (FastAPI or similar)
• Experience with vector stores, inference services, model-serving, and AI orchestration frameworks
• Familiarity with MLFlow, Kubeflow, or similar ML lifecycle tools
• Experience with API Gateway, JWT authentication, and enterprise security controls
• Comfort with Git-based workflows, CI/CD, containers, and Kubernetes

Nice to Have

• Experience building RAG solutions, MCP servers, or multi-agent orchestration frameworks
• Knowledge of LLM integration, prompt engineering, and model evaluation
• Familiarity with responsible AI, data governance, and self-service AI developer ecosystems