Senior Development Engineer (AI/ML Engineer Expertise)

North Risk Partners, LLC•Plymouth, MN

23h•$140,000 - $180,000

About The Position

This highly skilled Senior Development Engineer will design, build, and optimize systems that power analytics, AI Agentic workflows and business intelligence solutions. This role combines development expertise in enterprise systems, AI/ML development, and cloud technologies with hands-on experience in AI and machine learning workflows. In this role, you’ll design and deliver AI solutions that drive measurable business impact, set technical direction for AI initiatives, and influence cross-functional stakeholders. You will be at the intersection of engineering, product, and operations - connecting our strategic roadmap to outcomes.

Requirements

7+ years in development engineering with 2-4 years focused on AI/ML productization
Expert in Python and core ML/AI libraries (ex. PyTorch, TensorFlow)
Deep experience with LLMs (fine-tuning, prompt engineering, RAG, vector databases such as FAISS/pgvector/Pinecone)
Cloud proficiency: Azure (preferred), AWS, or GCP—covering model deployment, security, and cost optimization
Proven leadership: leading teams/projects, mentoring, and influencing executive stakeholders
Solid grasp of AI governance: responsible AI, security, privacy, compliance, and risk management
Strategic Thinking: Connects technology choices to business outcomes; crafts clear roadmaps
Technical Depth: You can both design and implement robust AI systems—end-to-end
Leadership & Coaching: Grows talent; creates clarity; builds trust; raises the bar
Influence & Communication: Tailors messaging to executives and engineers; drives alignment
Bias & Ethics Awareness: Proactive about responsible AI, data stewardship, and user trust
Ownership & Delivery: Moves from idea to impact; manages risks; meets commitments
Languages: Python, JavaScript, C#
ML/AI: PyTorch, TensorFlow
Data: Azure Data Lake/Databricks, SQL, Spark; vector DBs (FAISS, pgvector, Pinecone)
Cloud: Azure (Functions, AKS, App Service, Key Vault, Cosmos/SQL DB), Azure OpenAI

Nice To Haves

Experience integrating AI into enterprise platforms (e.g., Microsoft 365, Dynamics, Power Platform, Azure OpenAI) is preferred
Prior success delivering AI features at scale in production environments is preferred

Responsibilities

Design, develop, and maintain systems to support analytics, AI, and operational workflows
Leverage best practices for AI/ML data modeling, storage, and retrieval across structured and unstructured data sources
Optimize data workflows for AI/ML model training, inference, and deployment
Collaborate with data engineers, developers, and business stakeholders to ensure scalable system availability and quality for AI initiatives.
Develop best practices in use of data infrastructure in cloud environments (e.g., AWS, Azure, GCP)
Ensure compliance with data governance, security, and privacy standards
Troubleshoot and resolve systems and AI pipeline issues promptly
Support our AI strategy and roadmap aligned to business objectives and value creation
Architect scalable, secure AI systems (ML pipelines, model serving, data ingestion, monitoring) leveraging cloud-native services
Conduct build vs. buy assessments, vendor evaluations, and cost–benefit analyses for AI tooling and platforms
Establish model governance, risk controls, and ethical AI guidelines (bias monitoring, lineage, explain-ability, and human-in-the-loop)
Design, train, and deploy models (e.g., NLP, LLMs/RAG, computer vision, time-series forecasting, recommendation systems)
Optimize models for performance, latency, cost, and reliability, including prompt engineering and fine-tuning for LLMs
Integrate models into product and workflow experiences via APIs/microservices, event-driven architectures, and secure data pipelines
Partner across Product, Security, Legal, and HR to align delivery with compliance and change management
Communicate complex technical topics to executive and non-technical audiences; influence decisions with clarity and data
Develop guardrails, templates, and playbooks that accelerate safe, responsible AI use across the organization
Define KPIs & success metrics (e.g., model accuracy, adoption, cycle time, business impact, risk/incident rate)
Oversee observability: data drift, model decay, cost tracking, usage analytics, and incident response processes
Manage budgets, vendor relationships, and licensing for AI platforms and tools