1

Machine Learning Engineer Jobs in Cape Girardeau, MO

... machine learning, generative AI, agentic AI, and natural language processing required. * Strong understanding of large language models (LLMs), prompt engineering, retrieval-augmented generation (RAG ...

Software Engineer

Cape Girardeau, MO · On-site

$61K - $121K/yr

Summary In this role, you will apply software engineering best practices and your knowledge of ... Exposure to machine learning, natural language processing, Python, R, or SAS preferred. * You must ...

Multi Craft Technician (Maintenance)

Jackson, MO · On-site

$24.25 - $29.75/hr

Modify machine controls and operations * Repair heating and air conditioning equipment ... Programming digital AC and DC frequency drives * Calibrating sensors, overload protection devices ...

Multi Craft Technician (Maintenance)

Jackson, MO · On-site

$24.25 - $29.75/hr

Modify machine controls and operations * Repair heating and air conditioning equipment ... Programming digital AC and DC frequency drives * Calibrating sensors, overload protection devices ...

... Central Engineering, Scheduling, Shipping/Warehouse, and Sales & Marketing. Duties and ... Continuous Learning o Understands individual strengths and opportunity areas and seeks development ...

Machine Learning Engineer information

See Cape Girardeau, MO salary details

$30.3K

$123.9K

$186.1K

How much do machine learning engineer jobs pay per year?

As of Aug 28, 2026, the average yearly pay for machine learning engineer in Cape Girardeau, MO is $123,868.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,600.00 and $149,100.00 per year, depending on experience, location, and employer.

What is a machine learning engineer?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What does a machine learning engineer do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What are the key skills and qualifications needed to thrive as a machine learning engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

What are some common challenges faced by machine learning engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

What is the difference between Machine Learning Engineer vs Data Scientist?

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

What cities near Cape Girardeau, MO are hiring for Machine Learning Engineer jobs?

Cities near Cape Girardeau, MO with the most Machine Learning Engineer job openings:

Infographic showing various Machine Learning Engineer job openings in Cape Girardeau, MO as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $123,868 per year, or $59.6 per hour.

Lead Data Scientist

Vizient

Cape Girardeau, MO • On-site

Full-time

Re-posted 17 days ago


Job description

When you're the best, we're the best. We instill an environment where employees feel engaged, satisfied and able to contribute their unique skills and talents while living and working as their authentic selves. We provide extensive opportunities for personal and professional development, building both employee competence and organizational capability to fuel exceptional performance through an inclusive environment both now and in the future.

Summary:

In this role, you willdevelop and deploy advanced analytics, machine learning, generative AI, and agentic AI solutions that address complex business and client challenges. You will design scalable data science products, translate sophisticated analytical findings into actionable insights, and guide data scientists in delivering high-quality solutions that improve clinical, operational, and economic outcomes. You will influence technical strategy, drive innovation, and ensure analytical solutions align with organizational objectives.

Responsibilities:

  • Lead the design, development, and implementation of advanced machine learning, generative AI solutions across structured and unstructured data.
  • Build and deploy scalable, automated data science products and reusable analytic assets that support business and client objectives.
  • Analyze large-scale, high-dimensional datasets using modern analytics tools and distributed computing platforms to identify trends, opportunities, and actionable insights.
  • Design and prototype agentic AI workflows leveraging large language models (LLMs), retrieval systems, structured data, APIs, tools, and business rules to automate complex business processes.
  • Translate business requirements into AI agent architectures, including task decomposition, tool orchestration, routing logic, escalation paths, and human approval checkpoints.
  • Optimize retrieval-augmented generation (RAG) solutions through embedding evaluation, metadata design, reranking approaches, citation quality assessment, and knowledge freshness validation.
  • Develop evaluation frameworks for machine learning and AI solutions, including performance, accuracy, reliability, hallucination risk, latency, cost, safety, and consistency measures.
  • Make key analytical and architectural decisions, establish technical standards, and provide leadership on methodology, model selection, solution design, and AI implementation best practices.
  • Lead code reviews, mentor data scientists, and promote best practices in software development, responsible AI, model governance, and analytics delivery.
  • Partner with engineering, platform, and business teams to deploy, monitor, and maintain production-ready analytics and AI solutions.
  • Translate complex analytical findings into clear reports, visualizations, and presentations for technical and non-technical audiences.
  • Collaborate with stakeholders and clients to define analytical approaches that address strategic, operational, and clinical objectives.
  • Evaluate emerging technologies and industry trends to advance organizational analytics and AI capabilities.

Qualifications:

  • Relevant degree preferred. Advanced degree in applied mathematics, statistics, computer science, econometrics, or a related field is a plus.
  • 7 or more years of relevant experience required.
  • Demonstrated expertise in data science methodologies, statistical modeling, machine learning, generative AI, agentic AI, and natural language processing required.
  • Strong understanding of large language models (LLMs), prompt engineering, retrieval-augmented generation (RAG), and autonomous decision-making frameworks within enterprise AI environments highly preferred.
  • Expertise in programming languages such as Python, R, SQL, SAS, or similar analytical tools.
  • Experience with vector databases, semantic search technologies, knowledge graphs, metadata management, or enterprise search platforms.
  • Experience deploying, monitoring, and maintaining machine learning and AI solutions in production environments.
  • Knowledge of responsible AI principles, model governance, explainability, bias detection, validation methodologies, and ethical use of data.
  • Experience designing and implementing human-in-the-loop workflows that balance automation, governance, risk management, and user oversight.
  • Experience working with cloud-based and distributed data platforms and modern analytics frameworks such as Databricks.
  • Strong analytical, problem-solving, communication, and presentation skills with the ability to convey complex concepts to diverse audiences.
  • Experience leading complex analytics initiatives, providing technical guidance, and mentoring data scientists.
  • Experience working with healthcare data, including clinical, claims, operational, or financial datasets preferred.

#LI-JB1

Estimated Hiring Range:

At Vizient, we consider skills, experience, and organizational needs in our compensation approach. Geographic factors may adjust the range estimate and hires typically fall below the top range. Compensation decisions are tailored to individual circumstances. The current salary range for this role is $117,600.00 to $206,000.00.

This position is also incentive eligible.

Vizient has a comprehensive benefits plan! Please view our benefits here:

http://www.vizientinc.com/about-us/careers

Equal Opportunity Employer: Females/Minorities/Veterans/Individuals with Disabilities

The Company is committed to equal employment opportunity to all employees and applicants without regard to race, religion, color, gender identity, ethnicity, age, national origin, sexual orientation, disability status, veteran status or any other category protected by applicable law.