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Accenture

Advises businesses and runs their technology

AI / ML Engineer

AI Application EngineeringPosted on Nov 9
Full-timeMumbai, Maharashtra, IndiaNo salary listed

About the Job

Requirements

  • At least 3 years of experience is required; the additional information also specifies at least 5 years of experience with Large Language Models (LLMs).
  • Proficiency in Large Language Models (LLMs).
  • Strong proficiency in Python and production-level software engineering practices.
  • Hands-on experience building multi-agent systems using Google Agent Development Kit (ADK) or comparable agentic frameworks.
  • Experience deploying and customizing Gemini Enterprise and NotebookLM, and integrating the Google Workspace ecosystem through APIs, add-ons, and Drive/Docs connectors.
  • Experience managing agent session state, short-term context caching, and durable long-term memory backends such as vector databases and relational or NoSQL session stores.
  • Experience with Model Context Protocol (MCP) connectors for secure integration of third-party platforms.
  • Understanding of authentication and authorization protocols including IAM, OAuth 2.0, OIDC, service accounts, and Workload Identity Federation across Google Cloud and third-party SaaS platforms.
  • Working knowledge of enterprise access control lists (ACLs), identity federation, permission trimming, and role-based access control (RBAC) across heterogeneous data sources.
  • Proficiency with Agent CLI and Antigravity for local simulation, debugging, prompt engineering, and execution graph tracing.
  • Experience configuring and maintaining automated CI/CD pipelines, including Cloud Build and GitHub Actions, for agent containerization, deployment, and test automation.
  • Practical knowledge of AI evaluation methodologies and trajectory analysis, and ability to perform root cause analysis on agent execution failures, tool errors, and prompt regressions.
  • Technical communication, consulting, and client-facing collaboration skills within an Agile environment.
  • At least 15 years of full-time education is required.

Responsibilities

  • Design, develop, and deploy production-grade multi-agent systems and agentic workflows using Gemini Enterprise.
  • Determine whether pro-code or no-code design is appropriate based on customer problem statements and data sources.
  • Design, deploy, and customize agents using ADK and the Agent Platform AI Reasoning Engine, and integrate them with Gemini Enterprise.
  • Build native integrations across Google Workspace applications, including Docs, Drive, Gmail, Calendar, and Meet, as well as NotebookLM, for grounded enterprise research, document automation, and conversational discovery.
  • Architect scalable short-term session storage, conversation-state persistence, and long-term memory across multi-agent interactions.
  • Implement end-to-end authentication and authorization boundaries for agents, Google Cloud services, and third-party SaaS integrations using IAM, OAuth 2.0, OIDC, and service identities.
  • Enforce fine-grained ACLs, user and group permission inheritance, and data-governance policies so agents adhere to enterprise access boundaries.
  • Use Agent CLI and Antigravity for local prototyping, testing, emulation, distributed tracing, and root cause analysis.
  • Architect human-in-the-loop workflows, including interrupt and resume patterns between autonomous agents and human experts at critical decision points.
  • Build and maintain automated CI/CD pipelines, including Cloud Build, GitHub Actions, and GitLab CI, for versioned agent deployments, automated testing, and continuous regression evaluations.
  • Implement reasoning patterns such as Chain-of-Thought, ReAct, Plan-and-Solve, and Self-Reflection to improve agent reliability.
  • Establish quantitative evaluation frameworks to measure agent trajectories, tool-invocation precision and recall, and reasoning faithfulness.
  • Collaborate with client engineering and architecture teams to deploy, validate, and operationalize enterprise agentic AI solutions in client environments.

About the Company

Accenture

Advises businesses and runs their technology

Accenture advises large businesses and public agencies on strategy, builds their technology systems and runs ongoing operations. Its teams connect business planning with implementation, covering areas such as cloud migration, cybersecurity and customer experience. Consultants work with clients to redesign processes, while engineers integrate software from technology partners and maintain the resulting systems for multinational organizations. Clients pay for project work or continuing services, including application support and business processes such as finance and customer service. Accenture was founded in 1989 and has its headquarters in Dublin, Ireland.

Company size
10,001+
Company stage
IPO
Headquarters
Dublin, Ireland
Founded
1989

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