Technical Lead

Job Summary

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 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.

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 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.

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.
● 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).
● 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).
● 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