Senior Technical Specialist

Job description

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/recall, and reasoning faithfulness. ● Collaborate directly with client engineering and architecture teams to deploy, validate, and operationalize enterprise agentic AI solutions in client environments. Key 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/recall, and reasoning faithfulness. ● Collaborate directly with client engineering and architecture teams to deploy, validate, and operationalize enterprise agentic AI solutions in client environments. Skill Requirements Must-Have Skills ● 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 Collaboration: Strong technical communication, consulting acumen, and client-facing collaboration skills within an Agile environment. Good-to-Have Skills ● 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 ● 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. Other Requirements Certifications and Trainings ● Professional Machine Learning Engineer ● Google Skill Boost labs/course ○ Introduction to Gemini Enterprise : https://partner.skills.google/course_templates/1401 ○ Gemini Enterprise and NotebookLM : https://partner.skills.google/paths/3667 ○ Getting started with ADK: https://www.skills.google/catalog_lab/32017 ○ Deployment: https://www.skills.google/catalog_lab/32530 ○ Build and deploy multi agent: https://www.skills.google/course_templates/1445 Apply now Information at a Glance Why HCLTech? At HCLTech, you'll supercharge your potential. You'll find your career. And you'll find your spark. All at a place that knows that helping its customers stay