Gen AI Sr. Engineer

VeeRteq Solutions Inc
Denver, CO, United States
5 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
8 years minimum
Compensation
$66,400.0 - $96,200.0
Working hours
Regular working hours

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence Application Frameworks Software Applications Application Performance Management Unit Testing BigQuery Cloud Computing Cloud Storage Computer Programming Continuous Integration Software Debugging
+26 more
Monitoring of Systems Identity and Access Management Python (Programming Language) Object-Oriented Software Development Performance Tuning Systems Development Life Cycle Systems Integration AI Infrastructure Data Logging Google Cloud Enterprise Software Applications Large Language Models Multi-Agent Systems Technical Debt Generative AI Infrastructure as Code (IaC) Build Server Integration Tests Kubernetes Infrastructure Automation Frameworks Virtual Agents Restful APIs Terraform Serverless Computing Docker Microservices

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., 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., 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 VeeRteq Solutions is an Equal Opportunity Employer

Requirements

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

About the company

Jones Lang LaSalle

  • Broomfield, CO
  • $66,400-96,200 per year JLL empowers you to shape a brighter way. Our people at JLL are shaping the future of real estate for a better world by combining world class services, advisory and technology fo…, Prologis

  • Denver, CO
  • $121,000-159,000 per year At Prologis, we don’t just lead the industry-we define it with a 1.3 billion square foot portfolio and an annual throughput of approximately $3.2 trillion. We create the intelligen…

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on www.careerjet.com

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

2:07 min

Inspecting default bridge architectures and custom Docker networks

Oliver Seitz Oliver Seitz · WWC 2025

3:27 min

Explaining query execution overhead and caching limitations in BigQuery

Adnan Rahic · JS Congress

1:34 min

Essential commands for running and testing Terraform configurations

Hennie Francis · LIVE

2:18 min

Redefining developer roles and software architecture requirements

Laurie Voss · Coffee With Developers

2:32 min

Overview of Terraform and Terraform Cloud features

Devlin Duldulao · LIVE

2:30 min

Leveraging BigQuery ML for scalable SQL-based segmentation experiments

Julian Joseph · LIVE

Videos

See all

Related articles

See all