Senior Generative Ai Engineer Jobs in Minnesota (NOW HIRING)

Job Title : Lead Engineer (Generative AI)

Duration: 6-12 plus months

• Minneapolis/St. Paul “Twin Cities,” MN
• Bay Area, CA – San Francisco and surrounding areas
• Charlotte, NC
• Chicago, IL

Job Summary

The Lead Engineer (Generative AI) is a senior technical role responsible for designing, developing, and operationalizing enterprise-scale Generative AI (GenAI) solutions. This position combines deep hands-on expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic AI architectures with strong technical leadership to deliver secure, scalable, and resilient AI systems.

The role partners across engineering, product, and business teams to translate complex requirements into production-ready AI capabilities aligned with enterprise standards for security, risk, and responsible AI.

1. GenAI Solution Engineering
   • Design, develop, and deploy GenAI solutions leveraging:
     • Large Language Models (LLMs)
     • Retrieval-Augmented Generation (RAG) architectures
     • Prompt engineering techniques
     • Agentic AI workflows and orchestration
   • Build intelligent systems using frameworks such as LangChain, LangGraph, AWS Bedrock, and Microsoft Foundry Agent Service
   • Evaluate emerging tools and frameworks to continuously improve solution quality and innovation
2. GenAIOps & Lifecycle Management
   • Lead the end-to-end lifecycle of GenAI solutions, including:
     • Solution architecture and engineering
     • Integration with enterprise systems
     • Secure deployment and release management
     • Monitoring, observability, and continuous optimization
   • Implement GenAIOps best practices to ensure scalability, reliability, and cost efficiency
   • Establish logging, evaluation, and feedback mechanisms for production AI systems
3. Cloud, Platform & Scalability Engineering
   • Architect and deploy GenAI applications across cloud environments (Azure and AWS)
   • Design distributed systems capable of supporting high-throughput, low-latency AI workloads
   • Leverage modern infrastructure practices:
     • Containerization (Docker)
     • Orchestration (Kubernetes)
     • Infrastructure as Code (Terraform, ARM/Bicep)
   • Ensure high availability, performance, and enterprise-grade security
4. Software Engineering & Architecture
   • Develop scalable, maintainable applications using Python and microservices-based architectures
   • Apply secure coding standards and robust data handling practices for regulated environments
   • Build and manage CI/CD pipelines supporting automated testing, deployment, and release management
   • Enforce engineering best practices including code reviews, testing, and documentation
5. Technical Leadership & Influence
   • Provide architectural leadership and guidance across GenAI initiatives
   • Drive critical design decisions for large-scale, complex AI solutions
   • Mentor and coach senior engineers and development teams
   • Translate business requirements into scalable, secure, and resilient technical solutions
   • Partner with stakeholders across product, business, risk, and security functions

• Bachelor’s degree, or equivalent work experience
• Six to eight years of relevant experience

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field
• 8+ years of experience in software engineering, platform engineering, or AI/ML solutions
• 2+ years hands-on experience with GenAI technologies, including LLMs and RAG architectures and vector databases
• Strong knowledge of agentic AI concepts and frameworks (e.g., LangChain, LangGraph)
• Experience with cloud platforms (Azure and/or AWS)
• Deep understanding of distributed systems and scalable architecture patterns
• Proficiency in Python and microservices-based development
• Experience with Docker, Kubernetes, and Infrastructure as Code tools
• Demonstrated technical leadership and mentoring experience

• Experience implementing GenAI solutions in enterprise or regulated environments
• Familiarity with observability frameworks and AI lifecycle tooling
• Understanding of AI governance, security, and compliance requirements
• Experience contributing to or working with AI/ML or GenAI frameworks
• Background in financial services or other highly regulated industries

• Strong expertise in GenAI architectures and evolving AI technologies
• Ability to balance experimentation with enterprise-grade reliability

• Designs scalable, distributed, and resilient systems
• Aligns architecture decisions with enterprise standards and long-term strategy

• Drives end-to-end delivery from concept through production
• Ensures high standards for quality, security, and performance

• Influences without authority and leads through technical expertise
• Mentors engineers and elevates overall team capability

• Translates complex technical concepts into business outcomes
• Partners effectively across product, engineering, and leadership teams