Senior AI/ML Engineer

NMDP•Minneapolis, MN

18h•$120,000 - $150,000

About The Position

The Senior AI/ML Engineer will play a crucial role in the AI Center of Excellence (CoE), supporting cross-functional teams across the organization. This position will focus on designing, developing, and developing machine learning (ML), artificial intelligence (AI), Generative AI (GenAI) and Agentic AI solutions. As a member of the AI Center of Excellence, this role will focus on addressing strategic AI needs across a wide range of business domains, contributing to scalable, high-impact solutions that accelerate innovation and improve outcomes.

Requirements

Knowledge of Machine learning algorithms, deep learning frameworks, Cloud AI technologies, GenAI technologies and emerging Agentic AI technologies.
Knowledge of Cloud platforms (e.g., AWS, Azure, GCP) for scalable AI/ML development.
Knowledge of Responsible AI principles, including bias mitigation and ethical deployment.
Knowledge of ML Ops best practices including CI/CD for ML, model monitoring, and versioning.
Proficient in Python and common ML/AI libraries (e.g., TensorFlow, PyTorch, scikit-learn).
Strong understanding of data engineering, SQL, and feature engineering.
Direct experience with cloud services such as AWS Sagemaker, Lambda, ECS, S3, and IAM.
Familiarity with containerization (Docker) and orchestration (e.g., Airflow, Kubeflow).
Working with version control and collaboration tools (Git, Jira, Confluence).
Ability to build robust, scalable, and efficient AI/ML solutions in cloud-native environments.
Ability to translate ambiguous business problems into clear, technical ML/AI tasks.
Ability to communicate complex ideas clearly to technical and non-technical stakeholders.
Ability to learn and adapt quickly to emerging AI technologies, techniques, and tools.
A bachelor's degree in computer science, information technology, engineering, or a related field is required. However, equivalent related experience and/or education may be considered as a substitute for the degree requirement upon evaluation.
5+ years of experience designing and deploying ML/AI solutions in real-world environments.
Or a combination of 8 or more years of experience of software development/engineering, of which three or more years are AI/ML experience.
Proven experience with GenAI tools and technologies (e.g., LLMs, prompt engineering, Vector databases, RAG, fine-tuning)

Nice To Haves

Master’s or PhD in a related technical field.
Hands-on experience with agentic AI frameworks.
Prior contributions to open-source AI/ML projects or published research.
AI/ML certifications from cloud providers
Experience in highly regulated industries (e.g., healthcare, finance) a plus.

Responsibilities

Design, develop, and deploy production-grade traditional ML models (e.g., regression, classification, clustering, recommender systems) for a variety of business use cases.
Design, build and operationalize Gen AI (such as Retrieval Augmented Generation) and emerging Agentic AI solutions to address domain specific needs, improve user experiences and automate business workflows.
Design, maintain, and optimize end-to-end AI/ML pipelines including data ingestion, training, evaluation, deployment, and monitoring on cloud infrastructure (e.g., AWS or equivalent).
Integrate cloud-native and third-party AI SaaS solutions to accelerate delivery and reduce time to value.
Ensure AI/ML solutions are scalable, dependable, secure, and cost-effective within cloud environments.
Create reusable components, frameworks, and best practices to accelerate AI development.
Evaluating emerging AI technologies including GenAI and agentic AI (e.g., Model Context Protocol (MCP), Google’s Agent-to-Agent (A2A) protocol), as well as third-party low-code platforms) for their feasibility, scalability, and alignment with cross-functional business needs.
Solve for business problems by applying novel techniques and Innovating thinking.
Partner with data scientists, architects, product managers, business stakeholders, and technical teams across the organization to ensure AI solutions align with organizational goals.
Providing direct technical support and mentorship to technical teams across the enterprise is essential for enabling successful AI/ML implementations.
Enabling other teams to adopt AI Into their products
Leverage diverse skills and perspectives, leading to more effective problem-solving and decision-making.
Other duties as assigned.