Lead Agentic AI Engineer

Accenture in India; Ernakulam; In loco; INR 1.500.000 - 2.800.000; Tempo pieno

Descrizione del lavoro

Accenture in India seeks a Lead AI Engineer – Specialist to design and deploy production-grade AI solutions on Google Cloud, focusing on Vertex AI and Gemini models. You will lead backend, data engineering, and AI agent initiatives across product teams and DevOps in Kochi and nearby locations.

The role requires 5–8 years of experience in AI/ML, strong GCP expertise, and hands-on with Generative AI systems, RAG, and tool orchestration. AWS and Azure platform familiarity is a plus.

Competenze

• 5+ years of experience in AI/ML application development, backend engineering, data engineering, or related software engineering disciplines.
• Hands-on experience with Vertex AI and Gemini models, Generative AI applications, RAG, AI agents, APIs, and cloud-native development.
• Experience with Agentic AI concepts such as tool calling, orchestration, memory, MCP, A2A, and multi-agent systems is desirable.

Mansioni

• Design, build, and deploy production-grade 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, and enterprise integration.
• Build scalable architectures integrating Vertex AI with BigQuery, Cloud Storage, Cloud Run, GKE, Pub/Sub, and databases.
• Apply strong software engineering practices to develop secure APIs, microservices, and data pipelines for enterprise environments.
• Design, develop, test, and deploy Generative AI and LLM solutions using Google Cloud and Vertex AI.
• ImplementModel Context Protocol (MCP) clients/servers to access tools and data securely.

Conoscenze

GCP

Generative AI

Formazione

Graduation

Strumenti

AWS Bedrock

Azure AI Foundry

Azure OpenAI

Amazon SageMaker

Descrizione del lavoro

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-gradeAI, Generative AI, and Agentic AI applications on Google Cloud, primarily using Vertex AI and Gemini models.
• Develop intelligent AI applications and agents capableofreasoning, retrieval, tool use, workflow orchestration, structured output generation, task automation, and enterprise system integration.
• Build scalable AI application architectures integratingVertex 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 deployGenerative AI, LLM, Machine Learning, and Agentic AI solutions using Google Cloud Platform and Vertex AI.
• Build applications usingVertex 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 implementRetrieval-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 asBigQuery, 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.
• ImplementModel Context Protocol (MCP)clients or servers where applicable to provide standardized and secure access to tools, APIs, enterprise applications, and data sources.
• Work withAgent2Agent (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 asGoogle Agent Development Kit (ADK), LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or equivalent technologies.
• Evaluate and improve AI application quality acrossaccuracy, 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 includingGit-based development, automated testing, code reviews, CI/CD, infrastructure automation, documentation, security, and production release management.