AI Platform Engineer

What you will do

·        Build backend microservices, APIs, and enterprise integrations for AI and agentic apps

·        Run LLMOps/SLMOps: prompt and version management, evaluation, guardrails, deployment, monitoring, and inference optimisation

·        Develop AgentOps: orchestration, tool access, memory, observability, rollback, and governance

·        Ship via CI/CD, containers, and cloud designed for reuse through configuration, not rewrite

·        Own reliability, security, scalability, and performance across services and integrations

Tech: Python, REST, microservices, event-driven architecture · Docker, Kubernetes, CI/CD, cloud platforms such as AWS, GCP, or Azure · PostgreSQL, MongoDB, Redis, Kafka · Prometheus, Grafana · RAG, vector databases, and agent frameworks such as LangChain, LangGraph, or LlamaIndex, Fluent with AI tools for coding and data analysis such as Claude, Copilot, and Cursor.

You bring

·        5–6 years of production backend, platform, MLOps, or AI-systems engineering experience

·        Strong Python and hands-on experience with REST APIs, microservices, event-driven services, and enterprise integrations

·        Solid Docker, Kubernetes, CI/CD, cloud, and deployment practice

·        Good grasp of MLOps and SLMOps ,model lifecycle, evaluation, monitoring, and observability

·        Familiarity with agentic AI: orchestration, tool calling, prompt and version management, memory, guardrails, and human-in-the-loop workflows

Experience building at a product start-up is a strong plus : shipping from zero, owning components end to end, and moving fast with a small team is exactly the mindset we want.

Bonus: AI security patterns such as authentication, authorization, role-based access control, API gateways, and agent sandboxing · exposure to industrial AI, IoT, digital twins, or knowledge graphs as a plus

Education: Bachelor's or Master's in Computer Science, Engineering, Data Science from reputed Institute