AI/ML Engineer

NMDP•Minneapolis, MN

About The Position

The 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 to address domain specific needs, improve user experiences and automate business workflows. ACCOUNTABILITIES: (The primary functions, scope and responsibilities of the role) Engineering: Work across diverse GenAI platforms like AWS, Salesforce, Oracle, Snowflake, MS Copilot, and other 3rd party GenAI platforms and libraries. Automate workflows involving extraction of complex, multimodal unstructured content from variety of sources in to highly accurate and reliable structured content using platforms like AWS Textract and Bedrock Design and build MCP hosts, clients and servers Establish and use frameworks for automated LLM testing Create regression test suites to detect drift or prompt breakage Integrate with internal and external web services using secure authentication and authorization mechanisms Adopt and ensure safe practices to protect against prompt injections, jailbreaks, and conform to enterprise security guidelines Design, develop, and deploy production-grade traditional ML models (e.g., regression, classification, clustering, recommender systems) for a variety of business use cases. 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) Ensure AI/ML solutions are scalable, reliable, secure, and cost-effective within cloud environments Create reusable components, frameworks, and best practices to accelerate AI development Design and Innovation: Design and develop GenAI solutions using prompt engineering, Context Engineering, Retrieval-Augmented Generation (RAG), and custom pipelines Design and develop interoperable AI agents using Model Context Protocol (MCP) and/or Google A2A Collaboration and Enablement: Partner with data scientists, architects, product managers, business stakeholders and technical teams across organization to align AI solutions with organizational goals. Provide hands-on technical support and mentorship to technical teams across the enterprise.

Requirements

Machine learning algorithms, deep learning frameworks, Cloud AI technologies, GenAI technologies and emerging Agentic AI technologies.
Cloud platforms (e.g., AWS, Azure, GCP) for scalable AI/ML development.
Responsible AI principles, including bias mitigation and ethical deployment.
ML Ops best practices including CI/CD for ML, model monitoring, and versioning.
Build robust, scalable, and efficient AI/ML solutions in cloud-native environments.
Translate ambiguous business problems into clear, technical ML/AI tasks.
Communicate complex ideas clearly to technical and non-technical stakeholders.
Learn and adapt quickly to emerging AI technologies, techniques, and tools.
Bachelor’s degree in computer science, Engineering, or related field.
3+ years of experience designing and deploying ML/AI solutions in real-world environments in which time candidates will have created strong proficiency with the following:
Very strong Python skills with strong hands-on experience with LLM APIs (OpenAI, Azure OpenAI, Gemini, Anthropic, etc.) using Python and Python based frameworks
Strong hands-on experience in prompt engineering, context construction, grounding strategies
Strong hands-on experience with Retrieval Augmented Generation (RAG) extracting, chunking and create embeddings from unstructured documents from diverse sources including O365(email, word, excel), PDFs, and webpages.
Comfortable building Model Context Protocol (MCP) clients, servers and hosts.
Strong Expertise in building REST APIs and integrating with internal/external APIs
Hands-on experience with Intelligent Document Processing and/or OCR technologies on complex documents
Knowledge of Google A2A
Deep experience in AWS (Lambda, Bedrock, Step Functions, API Gateway, IAM)
Strong experience with Observability tools like Dynatrace, or other similar GenAI observability tools
Excellent GenAI foundations and concepts
Clear understanding of enterprise data privacy, AI governance, and observability
Proficiency in Python and common ML/AI libraries (e.g., TensorFlow, PyTorch, scikit-learn).
Strong understanding of data engineering, SQL, and feature engineering.
Hands-on 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 etc).

Nice To Haves

Master’s 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

Work across diverse GenAI platforms like AWS, Salesforce, Oracle, Snowflake, MS Copilot, and other 3rd party GenAI platforms and libraries.
Automate workflows involving extraction of complex, multimodal unstructured content from variety of sources in to highly accurate and reliable structured content using platforms like AWS Textract and Bedrock
Design and build MCP hosts, clients and servers
Establish and use frameworks for automated LLM testing
Create regression test suites to detect drift or prompt breakage
Integrate with internal and external web services using secure authentication and authorization mechanisms
Adopt and ensure safe practices to protect against prompt injections, jailbreaks, and conform to enterprise security guidelines
Design, develop, and deploy production-grade traditional ML models (e.g., regression, classification, clustering, recommender systems) for a variety of business use cases.
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)
Ensure AI/ML solutions are scalable, reliable, secure, and cost-effective within cloud environments
Create reusable components, frameworks, and best practices to accelerate AI development
Design and develop GenAI solutions using prompt engineering, Context Engineering, Retrieval-Augmented Generation (RAG), and custom pipelines
Design and develop interoperable AI agents using Model Context Protocol (MCP) and/or Google A2A
Partner with data scientists, architects, product managers, business stakeholders and technical teams across organization to align AI solutions with organizational goals.
Provide hands-on technical support and mentorship to technical teams across the enterprise.