Enter a job title or keyword
Senior Agentic AI Engineer – Google ADK Gemini GCP
Job Location:
Job Location:
Richardson, TX - USA
Monthly Salary:
Not provided by the employer
Monthly Salary:
Posted:
8 September 2026 (16 hours ago)
Posted:
Application Deadline:
6 December 2026
Application Deadline:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
Job Title: Senior Agentic AI Engineer Google ADK / Gemini / GCP
Work Location: Richardson TX 75082
**hybrid work set-up**
Must Have Skills:
Agentic AI Google ADK Gemini & Google Cloud
Good Communication Skill
Good Technical Skill
Nice to have skills:
Agentic AI Google ADK
Gemini & Google Cloud
Detailed Job Description:
We are seeking a skilled Senior Engineer - Agentic AI with hands-on expertise in Google Agent Development Kit (ADK) Gemini models and Google Cloud Platform. The successful candidate will build reliable agentic solutions that translate business requirements into scalable secure observable and maintainable production systems.
The role combines strong software engineering agent architecture evaluation cloud deployment and technical leadership. The engineer will own the end-to-end agent lifecycle from use-case discovery and design through production operations feedback-driven enhancement and governance.
Key Responsibilities
Agent Development & Engineering
Gemini & Generative AI Engineering
Cloud & Platform Engineering
Technical Leadership & Collaboration
Preferred Skills
Success Measures
Key Competencies
Minimum years of experience
5-8 years
Certifications Needed :No
Interview Process (face to face required)
Yes
Work Location: Richardson TX 75082
**hybrid work set-up**
Must Have Skills:
Agentic AI Google ADK Gemini & Google Cloud
Good Communication Skill
Good Technical Skill
Nice to have skills:
Agentic AI Google ADK
Gemini & Google Cloud
Detailed Job Description:
We are seeking a skilled Senior Engineer - Agentic AI with hands-on expertise in Google Agent Development Kit (ADK) Gemini models and Google Cloud Platform. The successful candidate will build reliable agentic solutions that translate business requirements into scalable secure observable and maintainable production systems.
The role combines strong software engineering agent architecture evaluation cloud deployment and technical leadership. The engineer will own the end-to-end agent lifecycle from use-case discovery and design through production operations feedback-driven enhancement and governance.
Key Responsibilities
Agent Development & Engineering
- Design develop test and deploy AI agents using Google ADK.
- Build single-agent and multi-agent systems for reasoning planning task execution collaboration and delegation.
- Develop custom tools function-calling interfaces workflow agents and integrations with enterprise APIs databases SaaS platforms and internal systems.
- Implement memory session and state management context management retrieval-augmented generation (RAG) and knowledge-grounding patterns.
- Apply deterministic workflows and dynamic agent orchestration patterns based on solution needs.
- Own the agent lifecycle from ideation prototyping and validation through release production operation retirement and replacement.
- Define versioning configuration release rollback and environment-promotion practices for agents prompts tools policies and models.
- Establish observability using structured logs traces metrics execution trajectories and error analysis.
- Implement security access control data protection governance and responsible AI controls.
- Improve reliability scalability resilience latency throughput and cost efficiency in production.
- Design automated and human-in-the-loop evaluation frameworks covering task success accuracy groundedness response quality safety tool-use effectiveness latency and cost.
- Create benchmark datasets test scenarios regression suites and release quality gates.
- Analyze agent behavior failed trajectories user feedback and production telemetry to identify improvement opportunities.
- Iterate on instructions prompts model selection tool design routing context strategies and orchestration.
- Run controlled experiments and document measurable quality improvements.
Gemini & Generative AI Engineering
- Use Gemini models for reasoning structured generation code assistance summarization tool calling and multimodal use cases.
- Apply prompt engineering structured outputs grounding safety controls token and context optimization and model-selection strategies.
- Balance solution quality latency reliability and cost across model and architecture choices.
Cloud & Platform Engineering
- Deploy and operate agents on GCP using appropriate services such as Vertex AI Agent Runtime Cloud Run Google Kubernetes Engine Cloud Functions BigQuery Pub/Sub Cloud Storage Cloud Monitoring and Cloud Logging.
- Implement CI/CD infrastructure as code automated testing secrets management IAM and environment controls.
- Apply AgentOps LLMOps MLOps SRE and cloud-native engineering practices to production AI systems.
Technical Leadership & Collaboration
- Lead design reviews and establish reusable engineering standards and reference patterns.
- Mentor engineers on ADK Gemini GCP agent evaluation AgentOps and responsible AI practices.
- Collaborate with product managers architects data scientists security teams and business stakeholders.
- Communicate technical trade-offs risks dependencies and outcomes to both technical and non-technical audiences.
Preferred Skills
- RAG architectures semantic search embeddings vector databases Vertex AI Vector Search and knowledge graphs.
- Model Context Protocol (ClientP) and secure tool or connector integration patterns.
- Experience with complementary agent frameworks such as LangGraph LangChain CrewAI AutoGen or similar technologies.
- Human-in-the-loop and approval-based workflows for high-impact actions.
- Terraform or equivalent infrastructure-as-code tooling.
- Responsible AI privacy threat modeling prompt-injection defense content safety and AI governance.
- Integration experience with enterprise platforms such as CRM ERP ITSM collaboration workflow and data platforms.
Success Measures
- Delivery of reliable secure and maintainable agents that meet defined business and technical requirements.
- Measurable improvement in agent task success groundedness response quality reliability latency and cost.
- Effective production monitoring incident reduction controlled releases and rapid root-cause analysis.
- Reusable engineering patterns strong documentation and increased team capability through mentoring.
- Positive stakeholder outcomes and clear alignment between agent capabilities and business value.
Key Competencies
- Systems thinking and solution architecture
- Strong analytical and problem-solving ability
- Experimentation and evidence-based improvement
- Technical ownership and engineering discipline
- Clear communication and stakeholder management
- Mentoring collaboration and influence
- Customer focus and responsible innovation
Minimum years of experience
5-8 years
Certifications Needed :No
Interview Process (face to face required)
Yes
Required Experience:
Senior IC
