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.

Professional and Technical Skills

• •Minimum 6 years of professional experience in backend development, data engineering, or a combination of both.
• •1–2 years of hands-on experience in Agentic AI, LLM application development, AI agents, RAG-based solutions, GenAI workflow automation, or multi-agent systems.
• •Strong hands-on experience developing applications and solutions on Google Cloud Platform (GCP).
• •Practical experience designing, developing, and deploying Generative AI, LLM-powered, RAG, Machine Learning, or Agentic AI applications.
• •Experience building production-grade APIs, microservices, data pipelines, AI services, cloud-native applications, or enterprise integration solutions.
• •Hands-on experience with Vertex AI and Gemini models for developing enterprise AI applications.
• •Experience integrating AI applications with enterprise databases, APIs, document repositories, cloud services, and external systems.
• •Experience deploying scalable, secure, reliable, and observable workloads within cloud environments.
• •Hands-on experience with GCP Vertex AI and the GCP cloud platform.
• •Strong understanding of Agentic AI concepts such as tool calling, planning, reasoning, memory, multi-agent workflows, orchestration, autonomous task execution, and agentic workflow design. Also Google ADK experience is must.
• •Experience working with MCP clients, MCP servers, tool registration, tool execution, context retrieval, and secure integration of external systems with LLM applications.
• •Experience with A2A-based or multi-agent communication patterns, including agent discovery, capability exchange, task handoff, inter-agent messaging, and collaborative workflow execution.
• •Experience with LLM application frameworks such as LangChain, LangGraph, Semantic Kernel, LlamaIndex, AutoGen, CrewAI, or similar frameworks.
• •Strong programming skills in Python; experience with Java, Node.js, or other backend technologies is an added advantage.
• •Experience developing backend services, REST APIs, microservices, event-driven applications, or integration layers.
• •Good understanding of Retrieval-Augmented Generation, embeddings, vector search, semantic search, chunking strategies, document ingestion, and prompt engineering.
• •Familiarity with vector databases or search platforms such as Azure AI Search, Amazon OpenSearch, Pinecone, Weaviate, FAISS, Chroma, Milvus, or similar tools.
• •Experience with Git-based development, code reviews, CI/CD pipelines, Docker, logging, monitoring, authentication, authorization, secrets management, and secure API integration.
• •Strong experience designing and developing scalable backend systems, services, APIs, data processing solutions, or enterprise integration layers.
• •Ability to integrate AI agents with databases, enterprise applications, third-party APIs, internal services, workflow systems, and external tools using protocols such as MCP where applicable.
• •Experience with data ingestion, transformation, validation, metadata handling, structured data processing, and unstructured document processing.
• •Good understanding of system design, performance optimization, error handling, observability, and production support.
• •Experience with AWS Bedrock, Azure AI Foundry, Azure OpenAI, Amazon SageMaker, or other AI platform