AI Engineer

Hollstadt Consulting is building AI capabilities through an AI Center of Excellence, bringing together cross-functional teams to deliver machine learning, AI, Generative AI, and Agentic AI solutions. This remote AI Engineer role supports domain-focused needs while helping translate complex requirements into production-ready systems across the organization.

In this position, you will work on end-to-end AI/ML pipelines and GenAI and agent capabilities, including building reliable workflows for extracting unstructured content and establishing testing and monitoring practices to keep models dependable over time.

Role focus

• Support cross-functional teams with machine learning, AI, Generative AI, and Agentic AI solutions
• Design and build production AI/ML pipelines and GenAI/agent capabilities for domain-specific requirements

Responsibilities

• Work across GenAI platforms and libraries including AWS, SalesForce, Oracle, Snowflake, MS Copilot, and other third-party GenAI offerings
• Automate workflows to extract complex, multimodal unstructured content from varied sources into accurate and reliable structured content using tools such as AWS Textract and Bedrock
• Design and build MCP hosts, clients, and servers
• Establish and use automated LLM testing frameworks
• Create regression test suites to detect drift and prompt breakage
• Integrate with internal and external web services using secure authentication and authorization mechanisms
• Adopt safe practices aligned with enterprise security guidelines to protect against prompt injections and jailbreaks
• Design, develop, and deploy production-grade traditional ML models such as regression, classification, clustering, and recommender systems
• Design, maintain, and optimize end-to-end AI/ML pipelines including data ingestion, training, evaluation, deployment, and monitoring on cloud infrastructure (for example, AWS or equivalent)
• Ensure AI/ML solutions are scalable, reliable, secure, and cost-effective in 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 to align solutions with organizational goals
• Provide hands-on technical support and mentorship to technical teams across the enterprise

Requirements

• 3+ years of experience designing and deploying ML/AI solutions in real-world environments
• Very strong Python skills
• Strong hands-on experience with LLM APIs (OpenAI, Azure OpenAI, Gemini, Anthropic, etc.) using Python and Python-based frameworks
• Strong hands-on experience with prompt engineering, context construction, and grounding strategies
• Strong hands-on experience with Retrieval Augmented Generation (RAG), including extracting, chunking, and creating embeddings from unstructured documents from sources such as O365 (email, Word, Excel), PDFs, and webpages
• Comfortable building Model Context Protocol (MCP) clients, servers, and hosts
• Strong expertise building REST APIs and integrating with internal and external APIs
• Hands-on experience with Intelligent Document Processing and/or OCR technologies on complex documents
• Knowledge of Google A2A
• Deep experience in AWS including Lambda, Bedrock, Step Functions, API Gateway, and IAM
• Strong experience with observability tools such as Dynatrace or 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 (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 (Airflow, Kubeflow)
• Working knowledge of version control and collaboration tools including Git, Jira, and Confluence
• Bachelor’s degree in computer science, Engineering, or a related field
• Knowledge of machine learning algorithms, deep learning frameworks, cloud AI technologies, GenAI technologies, and emerging agentic AI technologies
• Knowledge of cloud platforms (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
• 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

Technologies

• AWS, SalesForce, Oracle, Snowflake, MS Copilot
• AWS Textract, Bedrock, Model Context Protocol (MCP), Google A2A
• OpenAI, Azure OpenAI, Gemini, Anthropic
• REST APIs, Dynatrace
• TensorFlow, PyTorch, scikit-learn
• O365, PDFs, webpages
• Intelligent Document Processing, OCR
• AWS Lambda, AWS Step Functions, AWS API Gateway, AWS IAM, AWS Sagemaker, AWS ECS, AWS S3
• Docker, Airflow, Kubeflow
• Git, Jira, Confluence
• CI/CD, ML Ops
• Retrieval-Augmented Generation (RAG), prompt engineering, Context Engineering, grounding strategies, embeddings

Location, salary, and start details

• Location: Minnesota (remote), local preferred but not required
• Salary: USD 105,000 - 140,000 per year
• Additional compensation: 8% AIP eligibility
• Start date: 9/28/2026

Benefits

• Comprehensive benefit plan including medical, dental, vision, life insurance, short-term disability, long-term disability, paid sick leave, and retirement benefits
• 401(k) + matching (match on the first 4% of your contributions)
• Bonus opportunities including a Longevity Award bonus and referral bonus
• Professional development via on-demand training through a consultant portal, including upskilling in Artificial Intelligence (AI)
• Ongoing support and networking through a Consultant Coach program

Preferred qualifications

• Master’s degree 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 such as healthcare or finance