Forward Deployed Engineer (FDE) – Gemini Enterprise

HCL Technologies Limited; Bengaluru; On-site; INR 2,500,000 - 3,800,000; Full time

Job summary

HCL Technologies Limited seeks a Forward Deployed Engineer (FDE) with 8+ years to lead development of Gemini Enterprise-based multi-agent solutions. You will design MAS workflows, integrate with Google Workspace, and implement HITL security, IAM/OIDC, and advanced AI reasoning patterns.

You will collaborate with client teams to deploy scalable agentic AI in enterprise environments, ensuring governance, security, and reliable execution across platforms.

Qualifications

• 4 to 6+ years of experience in software development, cloud solution architecture, and AI engineering (preferably on Google Cloud Platform).
• Proven track record of designing, building, and deploying production AI agents or enterprise GenAI solutions using ADK, Vertex AI, or Gemini.
• Hands-on experience in customer-facing technical delivery, solutions engineering, or forward-deployed engineering (FDE) roles.
• Familiarity with enterprise Agile development lifecycles, sprint delivery, and stakeholder management.

Responsibilities

• Design, develop, and deploy production-grade Multi-Agent Systems (MAS) and agentic workflows using Gemini Enterprise.
• Identify need for Pro Code Vs No Code design based on customer’s problem statement and data sources.
• Design Deploy and custom agents using the Agent Development Kit (ADK), and Agent Platform AI Reasoning Engine and integrate with Gemini Enterprise.
• Build native integrations across the Google Workspace Ecosystem (Docs, Drive, Gmail, Calendar, Meet) and NotebookLM for grounded enterprise research, document automation, and conversational discovery.
• Architect scalable short-term session storage, conversation state persistence, and long-term memory architectures across multi-agent interactions.
• Implement end-to-end authentication and authorization perimeters for agents, Google Cloud services, and 3rd-party SaaS integrations using IAM, OAuth 2.0, OIDC, and service identities.
• Enforce fine-grained Access Control Lists (ACLs), user/group permission inheritance, and data governance policies to ensure agents strictly adhere to enterprise access boundaries.
• Utilize developer tooling including Agent CLI and Antigravity for local rapid prototyping, testing, emulation, distributed tracing, and root cause analysis (RCA).
• Architect Human-in-the-Loop (HITL) workflows, ensuring seamless Interrupt and Resume patterns between autonomous agents and human experts for critical decision points.
• Build and maintain automated CI/CD pipelines (e.g., Cloud Build, GitHub Actions, GitLab CI) to manage versioned agent deployments, automated testing, and continuous regression evaluations.
• Establish quantitative evaluation frameworks to measure agent trajectories, tool invocation precision/recal, and reasoning faithfulness.
• Collaborate directly with client engineering and architecture teams to deploy, validate, and operationalize enterprise agentic AI solutions in client environments.

Skills

Python

Gemini Enterprise

Workspace API

Memory architecture

MCP Connectors

IAM/OIDC

ACLs & RBAC

Developer tooling

CI/CD

AI evaluation

Client collaboration

Tools

ADK

NotebookLM

Google Workspace APIs

Vertex AI

CI/CD tools

Job description

Experience level - 8+ years to 20+ years

Project Description:

The system utilizes specialized LLM agents and Gemini Enterprise solutions to perform automated enterprise data retrieval, multi-step logical reasoning, and knowledge synthesis leveraging tools like NotebookLM and the Google Workspace Ecosystem. The platform integrates robust enterprise security, Access Control Lists (ACLs), and Human-in-the-Loop (HITL) protocols to securely connect disparate data silos (including third-party platforms like Microsoft Entra ID/M365, Salesforce, and Atlassian) while upholding organizational safety, security, and precision standards.

Role: Forward Deployed Engineer (FDE) specializing in Gemini Enterprise, Agentic AI, and Multi-Agent

Systems Responsibilities

• Design, develop, and deploy production-grade Multi-Agent Systems (MAS) and agentic workflows using Gemini Enterprise
• Identify need for Pro Code Vs No Code design based on customer’s problem statement and data sources
• Design Deploy and custom agents using the Agent Development Kit (ADK), and Agent Platform AI Reasoning Engine and integrate with Gemini Enterprise
• Build native integrations across the Google Workspace Ecosystem (Docs, Drive, Gmail, Calendar, Meet) and NotebookLM for grounded enterprise research, document automation, and conversational discovery.
• Architect scalable short-term session storage, conversation state persistence, and long-term memory architectures across multi-agent interactions.
• Implement end-to-end authentication and authorization perimeters for agents, Google Cloud services, and 3rd-party SaaS integrations (e.g., Microsoft Entra ID / M365, Salesforce, Atlassian) using IAM, OAuth 2.0, OIDC, and service identities.
• Enforce fine-grained Access Control Lists (ACLs), user/group permission inheritance, and data governance policies to ensure agents strictly adhere to enterprise access boundaries.
• Utilize developer tooling including Agent CLI and Antigravity for local rapid prototyping, testing, emulation, distributed tracing, and root cause analysis (RCA).
• Architect Human-in-the-Loop (HITL) workflows, ensuring seamless Interrupt and Resume patterns between autonomous agents and human experts for critical decision points.
• Build and maintain automated CI/CD pipelines (e.g., Cloud Build, GitHub Actions, GitLab CI) to manage versioned agent deployments, automated testing, and continuous regression evaluations. Implement advanced reasoning patterns such as Chain-of-Thought (CoT), ReAct, Plan-and-Solve, and Self-Reflection to enhance agent reliability.
• Establish quantitative evaluation frameworks to measure agent trajectories, tool invocation precision/reca l, and reasoning faithfulness.
• Colaborate directly with client engineering and architecture teams to deploy, validate, and operationalize enterprise agentic AI solutions in client environments.

Must-Have Skils

• Core Engineering & Frameworks: Strong proficiency in Python with production-level software engineering practices, along with deep, hands-on experience building multi-agent systems using Google ADK (Agent Development Kit) or comparable agentic frameworks.
• Gemini & Workspace Ecosystem: Solid hands-on experience deploying and customizing Gemini Enterprise, NotebookLM, and integrating with the broader Google Workspace Ecosystem (APIs, Add-ons, Drive/Docs connectors).
• Memory & State Architecture: Demonstrated expertise in managing agent session state, short-term context caching, and durable long-term memory backends (vector databases, relational/NoSQL session stores).
• Third Party Tools: Experience with MCP Connectors to securely integrate core third-party platforms (e.g., iManage, NetDocuments, Relativity, DocuSign, MS Office Suite, Sharepoint, JIRA) with Gemini Enterprise.
• Auth & Security: Strong understanding of authentication and authorization protocols (IAM, OAuth 2.0, OIDC, Service Accounts, Workload Identity Federation) across Google Cloud and 3rd-party SaaS platforms (Microsoft Entra ID, Salesforce, Atlassian).
• ACLs & Permissions: Deep working knowledge of enterprise Access Control Lists (ACLs), identity federation, permission trimming, and role-based access control (RBAC) across heterogeneous data sources.
• Developer Tooling: Proficiency in developer tooling such as Agent CLI and Antigravity for local simulation, debugging, prompt engineering, and execution graph tracing.
• CI/CD & DevOps: Proven track record of configuring and maintaining automated CI/CD pipelines (Cloud Build, GitHub Actions) for agent containerization, deployment, and test automation.
• Evaluation & Debugging: Practical knowledge of AI evaluation methodologies, trajectory analysis, and performing root cause analysis (RCA) on agent execution failures, tool errors, and prompt regressions.
• Client Colaboration: Strong technical communication, consulting acumen, and client-facing co laboration ski ls within an Agile environment.

Good-to-Have Skils

• Experience with inter-agent communication protocols such as Model Context Protocol (MCP) and Agent-to-Agent (A2A) architectures.
• Experience integrating agents with Enterprise Knowledge Graphs, structured ontologies, and hybrid vector search systems.
• Experience with enterprise data security frameworks such as Sensitive Data Protection (SDP/DLP) and VPC Service Controls (VPC-SC).

Qualifications and Prior Experience

• 4 to 6+ years of experience in software development, cloud solution architecture, and AI engineering (preferably on Google Cloud Platform).
• Proven track record of designing, building, and deploying production AI agents or enterprise GenAI solutions using ADK, Vertex AI, or Gemini.
• Hands-on experience in customer-facing technical delivery, solutions engineering, or forward-deployed engineering (FDE) roles.
• Familiarity with enterprise Agile development lifecycles, sprint delivery, and stakeholder management.

Certifications and Trainings

Professional Machine Learning Engineer

• Introduction to Gemini Enterprise : https://partner.ski ls.google/course_templates/1401
• Gemini Enterprise and NotebookLM : https://partner.skils.google/paths/3667
• Getting started with ADK: https://www.skils.google/catalog_lab/32017
• Build and deploy multi agent: https://www.skils.google/course_templates/1445