Senior AI Engineer

Description : Role : Senior AI Engineer

Location : Navi Mumbai, India

Only candidates residing in Mumbai will be considered.

Experience : 5-8 Years

We are looking for a Senior AI Engineer to design, build, and scale agentic AI systems powered by modern Large Language Models (LLMs). Reporting to our Principal AI Engineer, you will take technical ownership of complex AI workstreams, mentor more junior engineers, and help shape AI platform strategy.

This is a deeply hands-on role. You will architect and ship production-grade AI systems, multi-agent orchestration, RAG pipelines, and tool-augmented reasoning workflows while working across GCP, Azure, and AWS. We value versatile engineers who can navigate multiple frameworks and cloud environments to build resilient, observable, and scalable AI applications.

Requirements : What you will do : 1. Agentic AI & Multi-Agent Systems :

• Lead the implementation of multi-agent systems using Google AI SDKs (Vertex AI Agent Builder), LangGraph, CrewAI, and other emerging orchestration frameworks.

• Design and build stateful, tool-augmented agents capable of advanced reasoning, long-term planning, and autonomous execution.

• Develop and document agent orchestration patterns including planner-executor, supervisor-worker, and hierarchical agent structures.

• Implement sophisticated memory systems (short-term, long-term, and cross-session contextual memory).

• Enable seamless cross-agent communication and multi-modal coordination.

2. LLM Applications & Orchestration :

• Lead the delivery of production-grade LLM applications : RAG pipelines, specialised agents, and developer copilots.

• Integrate diverse tools, enterprise APIs, and legacy systems into agentic workflows.

• Design robust system prompts, dynamic routing logic, and AI guardrails using Vertex AI Model Garden or Azure AI Studio.

• Drive optimisation of AI workflows for latency, token cost, and output quality.

3. Platform & API Development :

• Develop and own reusable AI microservices, agent frameworks, and standardised APIs.

• Contribute to core AI platform capabilities including model routing, centralised observability, and safety filters.

• Define and enforce engineering standards and best practices for AI development across the team.

4. Cloud Deployment & Production Systems :

• Deploy and manage agent-based systems on GCP, Azure, and/or AWS using Docker, Kubernetes (GKE/AKS/EKS), and Cloud Run.