IN_Associate_AI Engineer_GCC_Advisory_Bangalore

Job Description & Summary:  

We are seeking a hands-on AI Engineer to design, build, integrate, and support production-grade AI applications powered by LLMs, agentic workflows, RAG, vector search, AI Gateway integrations, and cloud-native Azure services. The role requires strong experience in AI/ML engineering, backend development, prompt and context engineering, orchestration frameworks, and production readiness practices. 

The engineer will work closely with products, platform, MLOps, security, compliance, and application engineering teams to deliver secure, scalable, observable, and high-quality AI features for enterprise applications. 

Responsibilities: 

• Build application-facing AI features powered by LLMs, agentic workflows, and AI orchestration platforms. 

• Integrate with MLOps-managed agents and consume LLM capabilities through AI Gateway patterns including routing, authentication, policy controls, and logging. 

• Design and implement agentic workflows using tool calling, function calling, context handling, prompt/version management, and orchestration patterns. 

• Work with Agentic Layer A2A frameworks and MCP Protocol to enable interoperable agent communication and workflow execution. 

• Develop RAG-based solutions using vector embeddings, vector databases, Azure AI Search, Redis, Cosmos DB, and relevant cloud data stores. 

• Build and orchestrate LLM-powered applications using LangChain, LangGraph, and related frameworks. 

• Deploy scalable AI solutions on Azure using Azure Functions, Azure Container Apps, and cloud-native architecture patterns. 

• Optimize AI applications for latency, scalability, reliability, cost, and performance. 

• Implement production readiness practices including testing, observability, monitoring, evaluations, guardrails, safety controls, and incident support. 

• Partner with security, compliance, architecture, and platform teams to meet governance, data protection, and enterprise control requirements. 

• Document integration patterns, architecture, operational runbooks, evaluation practices, and reusable engineering components. 

Mandatory skill sets:   

• Experience building production AI integrations using LLMs, agents, orchestration frameworks, and APIs. 

• Hands-on experience with Agentic AI, A2A frameworks, MCP Protocol, tool/function calling, and workflow orchestration. 

• Strong expertise in vector embeddings, prompt engineering, prompt versioning, context engineering, and RAG. 

• Experience with LangChain and LangGraph. 

• Strong programming skills in Python and at least one backend language such as Java or Node.js. 

• Proficiency in Azure Cloud deployment. 

• Experience with Azure AI Search, vector databases, Redis, Cosmos DB, and related data platforms. 

• Proven ability to design and manage Azure Functions and Azure Container Apps. 

• Strong software engineering fundamentals including testing, observability, reliability, secure integration, and production support. 

• Familiarity with evaluation methods, guardrails, monitoring, and safety/quality controls. 

• Strong understanding of cloud-native architecture, scalability, and performance optimization. 

• Strong interpersonal / good negotiations skills are required. 

Preferred skill sets:   

• Experience with Blob Storage, Iceberg, Kubernetes, Docker, CI/CD, and MLOps pipelines. 

• Exposure to Azure AI Foundry, Azure OpenAI, AWS AI services, or multi-cloud AI platforms. 

• Experience with enterprise AI Gateway patterns, policy enforcement, and GenAI governance. 

• Experience building reusable AI starter kits, orchestration templates, evaluation harnesses, or observability frameworks. 

• Knowledge of responsible AI, data privacy, compliance, and secure enterprise AI delivery.  

Years of experience required: 

4 – 7 Years 

Education qualification:   

BTECH, MTECH