{"schemaVersion":"jobsearcher.job.v1","id":"2edde30ce34b5bea8ea303b5","url":"https://jobsearcher.com/jobs/2edde30ce34b5bea8ea303b5","canonicalUrl":"https://jobsearcher.com/jobs/2edde30ce34b5bea8ea303b5","title":"GenAI Engineer","description":"Seeking an experienced Gen AI Sr. Engineer to design, develop, deploy, and govern scalable Agentic AI solutions using Python, ADK, LLMs, multi-agent systems, GCP, Terraform, and CI/CD, while establishing architectural standards, AI governance, observability, and enterprise-grade AI application delivery.\nResponsibilities -\nAI Agent Development (40%) Design, develop, and maintain AI agents and AI-powered applications using Python and ADK. Develop reusable agent frameworks, orchestration workflows, and integrations. Build intelligent workflows leveraging LLMs, RAG, tool calling, and multi-agent systems.\nEnsure reliability, scalability, observability, and performance of AI agents. Cloud Deployment & Operations (25%) Deploy, monitor, and optimize AI applications and agents on Google Cloud Platform. Manage cloud-native services, APIs, compute resources, and AI infrastructure. Implement monitoring, logging, security, and operational best practices. Optimize infrastructure and application performance for cost and efficiency.\nCollaboration & Integration (15%) Partner with data scientists, analysts, architects, and business stakeholders.\nTranslate business requirements into AI-enabled technical solutions. Integrate AI agents into existing enterprise applications and workflows. Participate in solution design, architecture reviews, and stakeholder discussions. Infrastructure Automation (10%) Develop Infrastructure as Code (IaC) solutions using Terraform. Automate environment provisioning, deployments, and cloud configurations.\nImplement CI/CD pipelines supporting AI application lifecycles. Quality Engineering & Continuous Improvement (5%) Perform unit testing, integration testing, and troubleshooting. Improve agent evaluation, observability, and operational excellence.\nResolve production issues and optimize solution performance. Other Duties (5%) Support innovation initiatives and continuous learning. Contribute to AI best practices, standards, and reusable frameworks.\nEducational Qualifications: -\nEngineering Degree - BE/ME/BTech/MTech/BSc/MSc.\nTechnical certification in multiple technologies is desirable.\nSkills: -\nMandatory skills\nProgramming & Development Python (Expert level) Object-Oriented Programming (OOP) REST API Development Microservices Architecture Git / GitHub Agentic AI & Generative AI Agent Development Kit (ADK) Agentic AI solutions Multi-Agent Systems\nPrompt Engineering LLM Integration (Gemini, OpenAI, Claude, etc.) Tool Calling and Function Calling AI Agent Orchestration Frameworks Google Cloud Platform (GCP) Vertex AI Cloud Run Cloud Functions Cloud Storage Pub/Sub BigQuery IAM Monitoring & Logging Cloud Build Infrastructure & DevOps Terraform CI/CD Pipelines Docker Kubernetes (GKE) Infrastructure as Code (IaC) Testing & Operations Unit Testing Integration\nTesting Debugging & Troubleshooting Performance Optimization Monitoring & Observability L4 - L7 (Tech Lead Architect Principal)\nProven experience architecting and delivering systems using agentic IDEs Ability to: • Define architectural intent that agents can follow\nBreak features into agent executable tasks\nGovern AI autonomy (guardrails, permissions, reviews) • Integrate agentic workflows into CI/CD pipelines Experience supervising AI agents across:\nMulti service systems • Legacy modernization • Large codebases / monorepos Strong understanding of: • Security implications of autonomous code execution • Compliance, auditability, and traceability\nAI assisted SDLC operating models Core Responsibility: • Guide effective use of agentic IDEs for complex, multi-module or cross-service changes • Establish review practices and quality checks for AI-generated code\nMentor team members on balancing autonomy, correctness, and maintainability in AI-assisted development • Design system architectures that support AI-augmented and agentic development workflows\nDefine guardrails, standards, and governance for the use of autonomous coding agents • Evaluate impact of agentic IDEs on SDLC, CI/CD pipelines, security posture, and technical debt\n\nGenAI, AgenticAI, Python, ADK, VertexAI, Cloud","company":"Eapps Tech Dba Magicforce","rawCompany":"eapps tech dba magicforce","city":"Denver","state":"CO","isRemote":false,"isActive":false,"createdAt":"2026-08-04T23:27:27.513Z","occupations":[{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"17-2199.00","title":"Engineers, All Other","slug":"engineers-all-other"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"GenAI Engineer","description":"Seeking an experienced Gen AI Sr. Engineer to design, develop, deploy, and govern scalable Agentic AI solutions using Python, ADK, LLMs, multi-agent systems, GCP, Terraform, and CI/CD, while establishing architectural standards, AI governance, observability, and enterprise-grade AI application delivery.\nResponsibilities -\nAI Agent Development (40%) Design, develop, and maintain AI agents and AI-powered applications using Python and ADK. Develop reusable agent frameworks, orchestration workflows, and integrations. Build intelligent workflows leveraging LLMs, RAG, tool calling, and multi-agent systems.\nEnsure reliability, scalability, observability, and performance of AI agents. Cloud Deployment & Operations (25%) Deploy, monitor, and optimize AI applications and agents on Google Cloud Platform. Manage cloud-native services, APIs, compute resources, and AI infrastructure. Implement monitoring, logging, security, and operational best practices. Optimize infrastructure and application performance for cost and efficiency.\nCollaboration & Integration (15%) Partner with data scientists, analysts, architects, and business stakeholders.\nTranslate business requirements into AI-enabled technical solutions. Integrate AI agents into existing enterprise applications and workflows. Participate in solution design, architecture reviews, and stakeholder discussions. Infrastructure Automation (10%) Develop Infrastructure as Code (IaC) solutions using Terraform. Automate environment provisioning, deployments, and cloud configurations.\nImplement CI/CD pipelines supporting AI application lifecycles. Quality Engineering & Continuous Improvement (5%) Perform unit testing, integration testing, and troubleshooting. Improve agent evaluation, observability, and operational excellence.\nResolve production issues and optimize solution performance. Other Duties (5%) Support innovation initiatives and continuous learning. Contribute to AI best practices, standards, and reusable frameworks.\nEducational Qualifications: -\nEngineering Degree - BE/ME/BTech/MTech/BSc/MSc.\nTechnical certification in multiple technologies is desirable.\nSkills: -\nMandatory skills\nProgramming & Development Python (Expert level) Object-Oriented Programming (OOP) REST API Development Microservices Architecture Git / GitHub Agentic AI & Generative AI Agent Development Kit (ADK) Agentic AI solutions Multi-Agent Systems\nPrompt Engineering LLM Integration (Gemini, OpenAI, Claude, etc.) Tool Calling and Function Calling AI Agent Orchestration Frameworks Google Cloud Platform (GCP) Vertex AI Cloud Run Cloud Functions Cloud Storage Pub/Sub BigQuery IAM Monitoring & Logging Cloud Build Infrastructure & DevOps Terraform CI/CD Pipelines Docker Kubernetes (GKE) Infrastructure as Code (IaC) Testing & Operations Unit Testing Integration\nTesting Debugging & Troubleshooting Performance Optimization Monitoring & Observability L4 - L7 (Tech Lead Architect Principal)\nProven experience architecting and delivering systems using agentic IDEs Ability to: • Define architectural intent that agents can follow\nBreak features into agent executable tasks\nGovern AI autonomy (guardrails, permissions, reviews) • Integrate agentic workflows into CI/CD pipelines Experience supervising AI agents across:\nMulti service systems • Legacy modernization • Large codebases / monorepos Strong understanding of: • Security implications of autonomous code execution • Compliance, auditability, and traceability\nAI assisted SDLC operating models Core Responsibility: • Guide effective use of agentic IDEs for complex, multi-module or cross-service changes • Establish review practices and quality checks for AI-generated code\nMentor team members on balancing autonomy, correctness, and maintainability in AI-assisted development • Design system architectures that support AI-augmented and agentic development workflows\nDefine guardrails, standards, and governance for the use of autonomous coding agents • Evaluate impact of agentic IDEs on SDLC, CI/CD pipelines, security posture, and technical debt\n\nGenAI, AgenticAI, Python, ADK, VertexAI, Cloud","datePosted":"2026-08-04T23:27:27.513Z","dateModified":"2026-08-04T23:27:27.513Z","hiringOrganization":{"@type":"Organization","name":"Eapps Tech Dba Magicforce","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Denver","addressRegion":"CO","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"2edde30ce34b5bea8ea303b5"},"url":"https://jobsearcher.com/jobs/2edde30ce34b5bea8ea303b5"}}