Senior Technical Lead
Bangalore, Karnataka
Job Summary
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.
Key Responsibilities
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, 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
