GenAI Engineer

Magicforce; Denver (CO); On-site; USD 140,000 - 230,000; Full time

Job summary

Magicforce is seeking an experienced Gen AI Sr. Engineer to design, develop, deploy, and govern scalable agentic AI solutions using Python, ADK, LLMs, multi-agent systems, and GCP. The role emphasizes AI governance, observability, and enterprise-grade AI delivery across environments.

The candidate will design agent workflows, implement IaC with Terraform, and contribute to CI/CD pipelines, while collaborating with data scientists and stakeholders on architectural decisions.

Qualifications

• > Engineering degree required; multiple tech certifications desirable.
• < Core skills in Python, ADK, LLMs, agentic AI frameworks, and cloud platforms.
• < Proven experience delivering agentic AI solutions at scale.

Responsibilities

• Design, develop, deploy, and govern scalable agentic AI solutions.
• Build reusable agent frameworks, orchestration workflows, and integrations.
• Ensure reliability, observability, and performance of AI agents in production.

Skills

Python

ADK

LLMs

Multi-agent systems

GCP Vertex AI

Terraform

CI/CD

Observability

Education

Engineering Degree BE/ME/BTech

Tools

Docker

Kubernetes

Git/GitHub

Terraform

Job 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.

Responsibilities –

• AI 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.
• Ensure 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.
• Collaboration & Integration (15%) Partner with data scientists, analysts, architects, and business stakeholders.
• Translate 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.
• Implement 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.
• Resolve production issues and optimize solution performance.
• Other Duties (5%) Support innovation initiatives and continuous learning. Contribute to AI best practices, standards, and reusable frameworks.

Educational Qualifications: -

Engineering Degree - BE/ME/BTech/MTech/BSc/MSc.

Technical certification in multiple technologies is desirable.

Skills: -

Mandatory skills

• Programming & 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
• Prompt 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
• Testing Debugging & Troubleshooting Performance Optimization Monitoring & Observability L4 - L7 (Tech Lead - Architect - Principal)
• Proven experience architecting and delivering systems using agentic IDEs Ability to: Define architectural intent that agents can follow
• Break features into agent executable tasks
• Govern AI autonomy (guardrails, permissions, reviews) Integrate agentic workflows into CI/CD pipelines Experience supervising AI agents across:
• Multi service systems Legacy modernization Large codebases / monorepos Strong understanding of: Security implications of autonomous code execution Compliance, auditability, and traceability
• AI 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
• Mentor team members on balancing autonomy, correctness, and maintainability in AI-assisted development Design system architectures that support AI-augmented and agentic development workflows
• Define 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

GenAI, AgenticAI, Python, ADK, VertexAI, Cloud