Position Title - Lead Principal AI Software Development Engineer
Onsite role in St. Paul, MN Or Eagan, MN – Locals Preferred
Candidates authorized to work in the U.S. without sponsorship are encouraged to apply.
About the Role
This is a first-of-its-kind role on our engineering team. As Principal AI Software Development Engineer you will define what AI-assisted engineering looks like across the entire organisation — from tooling standards and agentic workflow design to developer coaching and AI governance. You are an engineer first: you build, prototype, and prove out practices yourself before scaling them to teams. This role sits at the intersection of software engineering, AI tooling, and organisational enablement within a growing renewable energy technology group.
Key Responsibilities
AI Engineering Strategy & Standards
– Define and govern enterprise AI engineering standards covering AI coding assistants, agentic pipelines, and responsible AI practices.
– Evaluate and optimise the organisation's AI technology stack: Vertex AI, Gemini, and Anthropic APIs.
– Establish guardrails for AI-generated code: quality gates, hallucination detection, security review, and compliance rules.
– Build the internal playbook for AI-native software delivery across all engineering teams.
Agentic Engineering & Prototyping
– Design and build agentic software engineering workflows: spec-driven coding, multi-agent pipelines, and automated review loops.
– Prototype AI-augmented solutions with business stakeholders to validate concepts rapidly.
– Build evaluation frameworks using Vertex AI and third-party tools to measure AI tool ROI and productivity impact.
– Integrate AI capabilities into GitLab CI/CD pipelines, code review, and automated testing workflows.
Developer Enablement
– Coach engineering teams on AI-native development using Gemini, Anthropic, and Vertex AI — hands-on, not classroom-based.
– Create reusable prompt libraries, agentic workflow templates, and AI integration patterns in Python and TypeScript.
– Drive grassroots adoption through hackathons, demos, and internal community of practice.
Platform Integration
– Integrate AI features into cloud-native services running on GKE; apply Microservices and Serverless patterns.
– Work with BigQuery for AI training data pipelines, evaluation datasets, and analytics.
– Ensure AI tooling integrates securely — enforcing authentication, authorisation, and data security principles.
Must-Have Skills
✓ 10+ years of software engineering with strong architecture and delivery experience.
✓ Deep hands-on expertise in AI-assisted software development — production use, measurable outcomes.
✓ Experience designing agentic engineering workflows: multi-step pipelines, quality gates, feedback loops.
✓ Advanced prompt engineering — structured prompts, chain-of-thought, evaluation-driven iteration.
✓ Strong Python proficiency (primary AI tooling language); TypeScript for full-stack integration.
✓ Practical experience with Vertex AI, Gemini API, and/or Anthropic API in production engineering contexts.
✓ Node.js and React for building AI-augmented product features and tooling interfaces.
✓ Google Cloud Platform and GKE for cloud-native AI workload deployment.
✓ BigQuery for data engineering: training data, evaluation pipelines, and analytics.
✓ GitLab CI/CD — integrating AI steps into pipelines, automating code review, and test generation.
✓ Authentication, authorisation, and data security principles applied to AI systems.
✓ Technical leadership and mentoring at team or organisational level.
RAG (Retrieval-Augmented Generation) design and implementation with vector databases.
AI evaluation frameworks: LangSmith, PromptFoo, or Vertex AI Evaluation.
Terraform for AI platform infrastructure provisioning.
Responsible AI and AI safety practices.
Serverless AI inference patterns on Google Cloud Platform.
Never miss an opportunity! Create an alert based on the job you applied for.
