Lead Agentic AI Engineer

Lead Agentic AI Engineer

Job Title - Lead AI Engineer – Specialist - ACS SONG 

Management Level: Level 9 - Specialist

Location: Kochi, Coimbatore, Trivandrum

Must have skills: GCP, Generative AI

Good to have skills: AWS Bedrock/ Azure AI Foundry/ Azure OpenAI / Amazon SageMaker or other AI platform

Experience: 5 -8 years of experience is required

Educational Qualification: Graduation

Job Summary

We are seeking a Senior AI Developer / Engineer specializing in Google Cloud Platform (GCP) with 5+ years of professional experience in AI/ML application development, backend engineering, data engineering, or related software engineering disciplines. The role will focus on designing, developing, and deploying production-grade Generative AI, Agentic AI, Machine Learning, and LLM-powered applications using Google Cloud technologies, with Vertex AI as the primary AI platform. 

 The ideal candidate should have strong hands-on experience with Vertex AI, Gemini models, Generative AI applications, RAG, AI agents, APIs, cloud-native application development, and enterprise integrations. Experience with Agentic AI concepts such as tool calling, orchestration, memory, MCP, A2A, and multi-agent systems is highly desirable. 

 The candidate will work closely with AI architects, data engineers, application developers, product teams, and DevOps engineers to build scalable, secure, observable, cost-efficient, and production-ready AI solutions. Experience with equivalent AI platforms such as AWS Bedrock or Azure AI Foundry is considered an additional advantage. 

Roles and Responsibilities

• Design, build, and deploy production-grade AI, Generative AI, and Agentic AI applications on Google Cloud, primarily using Vertex AI and Gemini models. 

• Develop intelligent AI applications and agents capable of reasoning, retrieval, tool use, workflow orchestration, structured output generation, task automation, and enterprise system integration. 

• Build scalable AI application architectures integrating Vertex AI with GCP services such as BigQuery, Cloud Storage, Cloud Run, GKE, Pub/Sub, API management, databases, and enterprise applications. 

• Apply strong software engineering principles to develop secure APIs, microservices, AI services, data pipelines, agent tools, and reusable AI components suitable for enterprise production environments. 

• Design, develop, test, and deploy Generative AI, LLM, Machine Learning, and Agentic AI solutions using Google Cloud Platform and Vertex AI. 

• Build applications using Vertex AI, Gemini models, Vertex AI APIs, embeddings, model endpoints, prompt management, grounding, function/tool calling, and other GCP AI capabilities. 

• Develop AI agents capable of planning, reasoning, tool calling, information retrieval, workflow execution, memory management, and multi-step task automation. 

• Design and implement Retrieval-Augmented Generation (RAG) solutions using Vertex AI, embeddings, vector search, enterprise documents, structured data, semantic search, and appropriate retrieval strategies. 

• Build integrations between AI applications and GCP services such as BigQuery, Cloud Storage, Cloud Run, Cloud Functions, GKE, Pub/Sub, Secret Manager, and other cloud-native services. 

• Develop backend APIs, microservices, connectors, integration services, and reusable tools that allow AI applications and agents to interact securely with enterprise systems, databases, APIs, and external services. 

• Implement Model Context Protocol (MCP) clients or servers where applicable to provide standardized and secure access to tools, APIs, enterprise applications, and data sources. 

• Work with Agent2Agent (A2A) patterns or protocols for agent discovery, task delegation, inter-agent communication, and multi-agent collaboration where required. 

• Work with AI/LLM orchestration frameworks such as Google Agent Development Kit (ADK), LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or equivalent technologies. 

• Evaluate and improve AI application quality across accuracy, groundedness, hallucination reduction, prompt quality, retrieval quality, latency, reliability, scalability, security, and cost efficiency. 

• Implement logging, monitoring, tracing, observability, evaluation, guardrails, and production support mechanisms for AI applications and agentic workflows. 

• Collaborate with architects, product owners, data engineers, backend developers, ML engineers, security teams, and DevOps teams to deliver enterprise-grade AI solutions. 

• Follow software engineering best practices including Git-based development, automated testing, code reviews, CI/CD, infrastructure automation, documentation, security, and production release management.