Senior AI Engineer - India
$150,000–$200,000 year
On-siteNavi Mumbai, Maharashtra, India
Job Summary
Architect and ship production-grade agentic AI systems, including multi-agent orchestration, RAG pipelines, and tool-augmented reasoning workflows across Google Cloud, Azure, and AWS. Lead the implementation of stateful agents with advanced memory and cross-agent coordination, while designing robust system prompts, dynamic routing logic, and AI guardrails. Develop reusable microservices and standardised APIs, then deploy and manage these systems using Docker, Kubernetes, and Infrastructure as Code. Drive incident response for production failures and mentor junior engineers through code reviews and design discussions. Collaborate with cross-functional teams to align AI engineering with business outcomes and shape the company's AI platform strategy.
Required Qualifications
- 5–8 years of software engineering experience
- at least 3 years focused on LLM-based or AI systems in production
- Proven track record building and shipping RAG pipelines, autonomous agents, and multi-step reasoning chains
- Strong hands-on experience with Google AI SDKs, Vertex AI, and/or Azure AI services
- Deep proficiency in orchestration stacks: LangGraph, CrewAI, LlamaIndex, Haystack, or comparable frameworks
- Expert-level Python
- strong backend development skills (FastAPI, Go, or Node.js)
- Deep understanding of agent design patterns: planning, reflection, memory, and tool-use
- Experience integrating complex enterprise APIs and event-driven systems into agentic workflows
- Proven ability to trace, debug, and improve non-deterministic, multi-step AI reasoning pipelines
- Strong instinct for building resilient, observable, and production-ready AI systems
- Strong familiarity with GCP and/or Azure core services: GKE, Cloud Run, Azure AI services
- Infrastructure as Code: Terraform or Pulumi
- CI/CD: experience building automated evaluation and deployment pipelines for AI models
Desired Qualifications
- Vector databases: Vertex AI Vector Search, Azure AI Search, Pinecone, or Weaviate
- Data pipelines: BigQuery, Pub/Sub, Azure Synapse
- ETL/ELT experience preparing unstructured data for RAG and fine-tuning
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