Lead Agentic AI Engineer 2

Accenture in India; Ernakulam; Vor Ort; INR 2.400.000 - 4.200.000; Vollzeit

Zusammenfassung

Accenture in India is seeking a Lead AI Engineer – Specialist with extensive experience in GCP, Vertex AI, and Generative AI. The role focuses on designing, building, and deploying production-grade AI/agentic AI solutions, leveraging Gemini models and enterprise integrations across Google Cloud platforms.

Responsibilities include architecting scalable AI systems, ensuring secure APIs and data pipelines, and collaborating with cross-functional teams to deliver cost-efficient, observable AI

Qualifikationen

• 5-8 years of experience required.
• Graduation is mandatory.

Aufgaben

• Design, build, and deploy production-grade AI/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, and enterprise system integration.
• Build scalable AI architectures integrating Vertex AI with BigQuery, Cloud Storage, Cloud Run, GKE, Pub/Sub, and databases.
• Apply software engineering principles to develop secure APIs, microservices, AI services, data pipelines, agent tools, and reusable AI components for production.
• Design and implement GenAI, LLM, ML, and Agentic AI solutions on GCP and Vertex AI.
• Develop AI agents capable of planning, reasoning, tool calling, information retrieval, and multi-agent collaboration.

Kenntnisse

GCP

Generative AI

Ausbildung

Graduation

Tools

Vertex AI

Gemini models

LangChain

APIs

Jobbeschreibung

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.