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Explainable Ai Jobs in Minnesota (NOW HIRING)

Sr Data Engineer BI

Bloomington, MN · On-site

$110K - $150K/yr

Champion responsible AI practices, ensuring solutions are explainable, secure, and aligned with QBP's data governance standards. Innovation & Emerging Technology [10%] * Champion innovation by ...

Sr Data Engineer BI

Bloomington, MN · Hybrid

$120K - $130K/hr

Champion responsible AI practices, ensuring solutions are explainable, secure, and aligned with QBP's data governance standards. Innovation & Emerging Technology [10%] * Champion innovation by ...

BI Application Manager

Bloomington, MN · Hybrid

$140K - $150K/hr

Champion responsible AI practices, ensuring solutions are explainable, secure, and aligned with QBP's data governance standards. Stakeholder Engagement & Executive Communication [10%] * Serve as the ...

Senior Machine Learning Engineer

Minneapolis, MN · On-site

$109K - $149K/yr

... with Explainable/Auditable AI/ML tools and interpretable model design • Experience with AI Software Development Tools (e.g., GitHub CoPilot, Claude) Company : Bringing online retail metrics ...

Explainable Ai information

What is the difference between Explainable Ai vs Data Scientist?

AspectExplainable AiData Scientist
CredentialsTypically requires knowledge of AI, machine learning, and data analysis; certifications like AI or ML courses are commonRequires degrees in computer science, statistics, or related fields; certifications in data analysis or machine learning are beneficial
Work EnvironmentWorks within AI development teams, focusing on model transparency and interpretabilityWorks across data analysis, model building, and business insights, often in research or corporate settings
Industry UsageUsed in AI development, healthcare, finance, and any field requiring transparent AI modelsApplied in tech, finance, healthcare, and research for data-driven decision making

Explainable Ai focuses on making AI models transparent and understandable, ensuring trust and compliance. Data Scientists develop and analyze models, often working with complex data. While both roles involve AI and data, Explainable Ai specialists emphasize interpretability, whereas Data Scientists focus on model creation and insights.

What are some of the typical challenges faced when working in Explainable AI and how do professionals address them?

Professionals in Explainable AI often encounter challenges such as balancing model accuracy with interpretability, translating complex model outputs into understandable insights for non-technical stakeholders, and ensuring transparency without compromising sensitive data. Addressing these issues typically involves using specialized tools and frameworks for visualization, collaborating closely with data scientists, domain experts, and business teams, and staying updated on the latest research in model interpretability. Continuous learning and open communication are key to overcoming these challenges and delivering AI solutions that are both effective and trustworthy.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as senior AI researcher, machine learning director, or AI solutions architect, often requiring advanced skills in data science, programming, and deep learning. These roles usually involve leadership responsibilities, strategic planning, and expertise in tools like Python, TensorFlow, or PyTorch, and may require relevant certifications or advanced degrees. Compensation at this level reflects significant experience and impact within the organization.

What degree is needed for XAI jobs?

Explainable AI (XAI) jobs typically require a bachelor's degree in computer science, data science, or a related field, with many roles preferring or requiring a master's or Ph.D. in artificial intelligence, machine learning, or a similar discipline. Strong programming skills, knowledge of machine learning frameworks, and understanding of model interpretability are also important for these roles.

What is the highest paying AI job?

The highest paying AI jobs typically include roles such as AI research director, machine learning engineer, and AI solutions architect, often requiring advanced degrees and expertise in deep learning, natural language processing, or computer vision. These positions can offer salaries exceeding $150,000 annually, especially in tech hubs or large organizations with specialized AI needs.

What are the key skills and qualifications needed to thrive as an Explainable AI specialist, and why are they important?

To thrive as an Explainable AI specialist, you need a strong background in machine learning, data science, and statistics, typically with an advanced degree in computer science or a related field. Familiarity with frameworks such as TensorFlow, PyTorch, and libraries like LIME or SHAP, as well as experience in model interpretability tools, is essential. Strong analytical thinking, effective communication, and the ability to translate complex technical concepts for non-technical stakeholders are crucial soft skills. These capabilities ensure that AI models are transparent, trustworthy, and can be responsibly integrated into decision-making processes.

What is Explainable AI?

Explainable AI (XAI) refers to methods and techniques in artificial intelligence that make the results of AI models understandable and interpretable by humans. XAI aims to provide transparency into how AI systems make decisions, helping users trust and effectively manage AI applications. This is especially important in fields like healthcare, finance, and law, where understanding the reasoning behind AI-driven outcomes can be crucial for accountability and compliance. By making AI more transparent, XAI also helps identify and address biases or errors in AI systems.

Which 3 jobs will survive AI?

Explainable AI specialists, data scientists, and AI ethics professionals are likely to continue thriving as AI advances, because their roles involve understanding, interpreting, and ensuring transparency of AI systems. These jobs require critical thinking, domain expertise, and communication skills that are difficult to automate fully. Continuous learning and familiarity with AI tools and frameworks are essential for these roles to remain relevant.
What cities in Minnesota are hiring for Explainable Ai jobs? Cities in Minnesota with the most Explainable Ai job openings:
Infographic showing various Explainable Ai job openings in Minnesota as of July 2026, with employment types broken down into 74% Full Time, 23% Part Time, and 3% Contract. Highlights an 71% Physical, 3% Hybrid, and 26% Remote job distribution.

Sr AI/ML Engineer - Remote Nationwide or Hybrid in MN/DC

UnitedHealth Group

Eden Prairie, MN • On-site, Remote

$106K - $146K/yr

Full-time

Medical, Retirement

Re-posted 29 days ago


UnitedHealth Group rating

7.6

Company rating: 7.6 out of 10

Based on 146 frontline employees who took The Breakroom Quiz

189th of 890 rated healthcare providers


Job description

OptumInsightis improving the flow of health data and information to create a more connected system. We remove friction and drive alignment between care providers and payers, andultimately consumers. Our deepexpertisein the industry and innovative technology empower us to help organizations reduce costs while improving risk management,qualityand revenue growth. Ready to help us deliver results that improve lives?Join us to startCaring. Connecting. Growing together.  

Come build the AI foundation behind SGS, a modern platform where multi-agent systems can reason, plan, and safely connect to enterprise tools to turn conversations, operational signals, and network patterns into action. In this role, you will own the architecture for the capabilities customers feel every day, including real-time voice translation, voice-based assessments, and call quality audits that help teams respond faster and follow the right procedures. You will also advance our Virtual Investigator to accelerate intake, surface risk early, and turn complex investigative questions into clear, explainable answers.

On the fraud side, you will combine real-time ML, graph analytics, and explainable AI to detect emerging fraud, waste, and abuse across claims, enrollment, provider, and encounter data; uncover collusive networks and billing anomalies; and translate those signals into defensible investigative leads, preventive controls, and KPI visibility leaders can use immediately. You will set the technical direction for orchestration and governance on Azure, raise engineering standards across product lines, and mentor a globally distributed team that ships to production. If you want deep ownership, high-stakes impact, and the chance to define how agentic AI operates at scale in a highly regulated healthcare environment, you will thrive here.

You'llenjoy the flexibility to work remotely * from anywhere within the U.S. as you take on some tough challenges. For all hires in the Minneapolis or Washington, D.C. area, you will be required to work in the office a minimum of four days per week.

Primary Responsibilities:  

  • AI Engineering Projects: Design and implement multi-agent AI systems that use LLMs, memory, and tools to reason, plan, and act autonomously
  • Function Calling & Orchestration: Build agent-based solutions that use function calling, dynamic tool integration, and orchestration frameworks such as LangChain, AutoGen, and Semantic Kernel
  • Modular Agent Design: Leverage standards such as Model Context Protocol (MCP) to define reusable, secure, and composable tool interfaces
  • Voice-Driven Interfaces: Develop voice-first AI agents using ASR technologies such as Whisper and Azure Speech, multi-turn conversation orchestration, and high-quality TTS
  • RAG & Memory Pipelines: Design and maintain retrieval and memory pipelines using vector databases and Azure Cognitive Search to ground agents in enterprise knowledge, prior interactions, and operational context
  • Fraud, Waste & Abuse Analytics & ML Ops: Design, build, and operationalize supervised and unsupervised models, including classification, clustering, anomaly detection, risk scoring, and graph/network analysis, to detect known and emerging FWA patterns across claims, enrollment, provider, and encounter data. Translate fraud typologies such as upcoding, unbundling, excessive units, duplicate or phantom billing, kickbacks, and encounter discrepancies into scalable model logic, rules, and real-time detection pipelines. Continuously refine detection effectiveness using referral, audit, and recovery outcomes where available
  • SQL & Data Engineering for FWA: Develop and optimize complex SQL queries, feature pipelines, and data validation checks for large-scale healthcare analytical workflows, including joins, window functions, aggregations, and performance-aware query design
  • Cloud-Native AI Deployment: Build, deploy, and monitor scalable AI services on Azure, including Azure OpenAI, Functions, Service Bus, Cosmos DB, Cognitive Search, and related tools
  • Explainability, Investigations & Governance: Produce clear, reproducible model outputs, narratives, visualizations, and KPI reporting that support investigators, clinicians, compliance teams, and business leaders. Contribute to model governance, validation, and documentation practices that ensure transparency, fairness, and regulatory defensibility
  • Agentic UX & AI as an Interface: Drive innovation in agentic user experiences, enabling AI to operate external tools and services securely on behalf of users
  • Mentorship & Collaboration: Review PRs, mentor junior engineers, and collaborate across India and US time zones in a distributed, agile environment

You'llbe rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well asprovidedevelopment for other roles you may be interested in.

Required Qualifications: 

  • Undergraduate degree or equivalent experience 
  • 5 years of total engineering experience 
  • 5 years of experience in AI/ML product engineering roles 
  • 5 years of solid Python development experience; proficiency with ML frameworks such as PyTorch, scikit-learn, and Hugging Face 
  • 5 years of experience with the Azure AI stack, including Azure OpenAI, Cognitive Services, Functions, Service Bus, and Cognitive Search 
  • 5 years of experience with fraud detection, anomaly detection, risk scoring, or graph/network analytics pipelines 
  • 3 years of solid experience with voice systems, including ASR, TTS, and real-time audio or telephony integration 
  • 2 years of proven experience building and shipping LLM-powered or autonomous agent systems in production 
  • 2 years of deep experience with LLM integration, tool calling, prompt engineering, and context-aware task execution 
  • 2 years of hands-on experience with retrieval techniques such as RAG, semantic search, embeddings, and vector databases 
  • Proven solid SQL development skills, including complex joins, window functions, aggregations, and performance optimization for analytical workloads 
  • Experience working with healthcare claims, provider, enrollment, encounter, or other highly regulated transactional healthcare datasets 
  • Demonstrated ability to explain model behavior, risk signals, and analytic findings to nontechnical stakeholders through clear, defensible documentation 
  • Demonstrated track record of contributing to robust, testable, and scalable engineering systems

 

Preferred Qualifications: 

  • Experience building automated evaluation harnesses for LLM and agent workflows, including golden datasets, offline and online testing, and measurable quality metrics such as task success rate, groundedness, or human-review agreement 
  • Hands-on experience with responsible AI, including prompt injection testing, data exfiltration testing, safety reviews, and guardrails to reduce hallucinations and unsafe outputs in production 
  • Proven experience implementing end-to-end observability for agentic systems, including distributed tracing, tool-call success rates, latency and error budgets, and token and cost telemetry with actionable alerting 
  • Proven experience designing secure patterns for tool-enabled agents, including least-privilege access, secrets management, and policy-based controls for tool or API execution such as OAuth scopes, managed identity, and audit logging 
  • Proven ability to optimize LLM or voice-system performance and cost using techniques such as caching, batching, streaming responses, rate limiting, model routing, and fallback strategies 
  • Direct experience supporting Medicaid program integrity, healthcare fraud analytics, State Medicaid Agency analytics, or MCO SIU workflows 
  • Familiarity with Medicaid reimbursement and billing constructs, including ICD-10, CPT/HCPCS, DRGs, revenue codes, NDCs, and encounter data 
  • Familiarity with MMIS, T-MSIS, PERM, CMS program integrity guidance, or similar state or federal compliance frameworks 
  • Experience supporting referral, recovery, audit, appeal, or case-prioritization workflows 
  • Experience developing AI/ML solutions in regulated or government healthcare environments

*All employees working remotely will berequiredto adhere to UnitedHealth Group's Telecommuter Policy

Pay is based on several factors including but not limited to local labor markets, education, work experience, certifications, etc. In addition to your salary, we offer benefits such as, a comprehensive benefits package, incentive and recognition programs, equity stock purchase and 401k contribution (all benefits are subject to eligibility requirements). No matter where or when you begin a career with us, you'll find a far-reaching choice of benefits and incentives. The salary for this role will range from $120,100 to $214,500 annually based on full-time employment. We comply with all minimum wage laws as applicable.

Application Deadline: This will be posted for a minimum of 2 business days or until a sufficient candidate pool has been collected. Job posting may come down early due to volume of applicants.

At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone-of every race, gender, sexuality, age,locationand income-deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalizedgroupsand those with lower incomes. We are committed to mitigating our impact on the environment and enabling and deliveringequitablecare that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.

 

UnitedHealth Group is an Equal Employment Opportunity employer under applicable law and qualified applicants will receive consideration for employment without regard to race, national origin, religion, age, color, sex, sexual orientation, gender identity, disability, or protected veteran status, or any other characteristic protected by local, state, or federal laws, rules, or regulations.

 

UnitedHealth Group is adrug -free workplace. Candidatesare required topass a drug test before beginning employment.

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