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Mlops Jobs in Indiana (NOW HIRING)

As a Staff ML Engineer, you'll focus on the MLOps and infrastructure layer that makes ML production-ready: model serving, feature pipelines, experiment tracking, and CI/CD for ML. You'll help shape ...

As a Staff ML Engineer, you'll focus on the MLOps and infrastructure layer that makes ML production-ready: model serving, feature pipelines, experiment tracking, and CI/CD for ML. You'll help shape ...

As a Staff ML Engineer, you'll focus on the MLOps and infrastructure layer that makes ML production-ready: model serving, feature pipelines, experiment tracking, and CI/CD for ML. You'll help shape ...

Data Systems/Solutions Engineer

Indianapolis, IN · On-site

$109K - $131K/yr

DataOps / MLOps Enablement: * Implement CI/CD practices for data and ML workflows, including testing, version control, and environment promotion. * Support reproducible analytics and ML pipelines ...

You will work closely with data scientists, MLOps engineers, system owners, risk stakeholders, and government counterparts to define validation standards, execute reviews, and ensure alignment with ...

You will work closely with data scientists, MLOps engineers, system owners, risk stakeholders, and government counterparts to define validation standards, execute reviews, and ensure alignment with ...

End-to-End MLOps and Deployment: Own the entire engineering lifecycle for central, reusable computer vision models and foundational AI infrastructure. This includes establishing best-in-class MLOps ...

... MLOps teams to standardize CI/CD practices for AI models. • Conduct technical reviews of proof of concepts, architecture diagrams, and production implementations. • Evaluate AI solutions for ...

Data Architect

Indianapolis, IN · On-site

$61 - $78.50/hr

Partner with AI/ML, MLOps, and analytics teams to enable productiongrade model development and deployment * Guide implementation teams through complex architectural decisions and tradeoffs * Support ...

Lead Engineer

Indianapolis, IN · On-site

$97K - $129K/yr

The engineer will operate at the intersection of distributed systems engineering, applied machine learning infrastructure, AI security, and MLOps, translating experimental NLP and generative AI ...

Data Architect

Indianapolis, IN · On-site

$61 - $78.25/hr

Partner with AI/ML, MLOps, and analytics teams to enable production‑grade model development and deployment * Guide implementation teams through complex architectural decisions and trade‑offs

Senior AI/ML Engineer

Bedford, IN · On-site

$93K - $128K/yr

Demonstrated experience with LLMs, MLOps pipelines, and modern ML frameworks (e.g., PyTorch, TensorFlow). * Strong background in software and cyber engineering principles, including system hardening ...

Work closely with the MLOps team to create and maintain robust evaluation solutions and tools to evaluate model performance, accuracy, consistency, reliability, during development, and UAT.

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Showing results 1-20

Mlops information

See Indiana salary details

$95.2K

$149.3K

$177.6K

How much do mlops jobs pay per year?

As of Jun 17, 2026, the average yearly pay for mlops in Indiana is $149,320.00, according to ZipRecruiter salary data. Most workers in this role earn between $141,083.00 and $162,176.00 per year, depending on experience, location, and employer.

What is the difference between Mlops vs Data Engineer?

AspectMlopsData Engineer
Primary FocusDeploying, managing, and monitoring machine learning models in productionBuilding and maintaining data pipelines and infrastructure for data processing
Skills & CertificationsMachine learning, DevOps, cloud platforms, scriptingSQL, ETL, data warehousing, programming
Work EnvironmentCollaborates with data scientists, software engineers, and DevOps teamsWorks with data analysts, data scientists, and software developers
Industry UsageAI/ML projects, production environments, cloud servicesData infrastructure, analytics, big data processing

While both Mlops and Data Engineers work closely with data and cloud technologies, Mlops specialists focus on deploying and maintaining machine learning models in production, ensuring their scalability and reliability. Data Engineers primarily build data pipelines and infrastructure to support data analysis and ML workflows. Understanding these distinctions helps organizations assign the right roles for their AI and data projects.

What are the key skills and qualifications needed to thrive as an MLOps Engineer, and why are they important?

To thrive as an MLOps Engineer, you need a strong background in machine learning, software engineering, and DevOps principles, often supported by a degree in computer science or a related field. Proficiency with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (e.g., AWS, Azure, GCP), and ML frameworks is typically required, along with certifications in cloud or DevOps technologies. Strong problem-solving skills, collaboration, and communication abilities help MLOps professionals excel in cross-functional teams and manage complex workflows. These skills are vital for reliably deploying, monitoring, and scaling machine learning models in production environments, ensuring efficiency and robustness.

Is MLOps a good career path?

MLOps is a growing field that combines machine learning, software engineering, and operations to deploy and maintain AI models efficiently. It offers high demand for skills in cloud platforms, automation, and data management, making it a promising career choice for those interested in AI infrastructure. Professionals in MLOps often work with tools like Docker, Kubernetes, and CI/CD pipelines, and typically require a strong understanding of both machine learning and software development.

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

MLOps professionals often encounter challenges such as ensuring reproducibility of models, managing version control for both code and data, and maintaining model performance over time. Handling continuous integration and deployment (CI/CD) pipelines for ML models can be complex, especially when dealing with large datasets and evolving algorithms. Additionally, coordinating with data scientists, software engineers, and DevOps teams to streamline workflows and monitor models post-deployment are key responsibilities that require both technical expertise and strong collaboration skills.

What engineers make $500,000?

Senior machine learning operations (MLOps) engineers with extensive experience, specialized skills in cloud platforms, automation, and deployment often reach or exceed $500,000 annually in total compensation. High-level roles in tech companies or those with leadership responsibilities and advanced certifications tend to offer such salaries.

Which 3 jobs will survive AI?

For MLOps professionals, roles such as data scientists, machine learning engineers, and AI infrastructure engineers are expected to persist as AI adoption grows. These jobs require specialized skills in model development, deployment, and maintenance that complement automation. Continuous learning and expertise in tools like Kubernetes, cloud platforms, and version control are essential for long-term viability.

What is a $900,000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as senior machine learning engineer or AI director, often requiring advanced skills in data science, deep learning, and cloud platforms. These roles usually involve leadership, strategic planning, and extensive experience, and they may include bonuses or stock options that contribute to the total compensation. Such salaries are rare and generally found in large tech companies or specialized AI firms.

What are MLOps?

MLOps, short for Machine Learning Operations, is a set of practices that combines machine learning, DevOps, and data engineering to automate and streamline the deployment, monitoring, and maintenance of machine learning models in production. MLOps aims to improve collaboration between data scientists and operations teams, ensuring that models are robust, scalable, and easily updated. It covers the entire machine learning lifecycle, from data preparation to model training, deployment, and ongoing monitoring. By implementing MLOps, organizations can accelerate the development and deployment of reliable machine learning solutions.
What are the most commonly searched types of Mlops jobs in Indiana? The most popular types of Mlops jobs in Indiana are:
What are popular job titles related to Mlops jobs in Indiana? For Mlops jobs in Indiana, the most frequently searched job titles are:
What cities in Indiana are hiring for Mlops jobs? Cities in Indiana with the most Mlops job openings:

MLOps Automation Senior Lead Engineer

Huntington

Hagerstown, MD • On-site, Remote

$97K - $127K/yr

Full-time

Medical, Life, Retirement, PTO

Posted 5 days ago


Job description

Description

Summary:

The MLOps Automation Engineering Senior Lead will lead a team responsible for building and deploying MLOps Automation for some of Huntington’s most valuable and most challenging data-driven projects.

Duties and Responsibilities:

  • Streamline the data, analytics, and model development lifecycle by identifying pain points and productivity barriers and determining ways to resolve them through automation.
  • Helps set the strategy and tone for MLOps Automating Engineering strategy and vision for the future.
  • Understand the current process and technical complexities of developing and deploying data pipelines and model builds and develop automation solutions to improve and extend the existing process to become an unattended delivery pipeline.
  • Collaborate closely with product development, architecture, data engineering and testing teams to understand their current build and release processes and make recommendations for improvement through the automation of various tasks.
  • Partner with cross-functional stakeholders, including development, operations, quality assurance and security, to streamline processes.
  • Develop and continuously improve automation solutions to enable teams to build and deploy quality data and code efficiently and consistently.
  • Build automated testing solutions in support of quality management objectives to reduce manual effort.
  • Build automated environment provisioning solutions in response to changes in processing demand. 
  • Build automated feedback mechanisms to monitor the performance of models in production.
  • Work closely with cross-functional stakeholders to analyze and troubleshoot complex production issues. 
  • Prepare and present design and implementation documentation to multiple stakeholders.
  • Promote automation across the data management and analytics delivery organization.
  • Perform other duties as assigned.

Basic Qualifications:

  • Bachelor’s Degree (Computer Science, Business Administration, Economics or related fields) or equivalent relevant work experience
  • 10+ years of relevant automation engineering experience, of software engineering, in strategy, management consulting, or similar skillset, and of technical leadership experience with data-centric products 
  • 10+ years of experience with one or more coding languages (e.g., JavaScript, C++, Python, Java), CI/CD tools (e.g., Jenkins, Artifactory, CircleCI, Ansible), and development platforms (e.g., AWS, Azure, Docker, Kubernetes)

Preferred Qualifications:

  • Strong collaboration skills, with a demonstrated ability to work well as part of a team
  • Experience developing CI/CD workflows and tools
  • Strong automation scripting skills
  • Experience in configuration management, test-driven development, and release management.
  • Strong analytical and troubleshooting skills.
  • Experience with agile development and strong understanding of DataOps and ModelOps principles
  • Ability to investigate and analyze information, and to draw conclusions
  • Flexibility, adaptability, and desire to learn new languages and technologies
  • Strong verbal and written communication skills
  • Demonstrated ability to work independently across multiple tasks while meeting aggressive timelines
  • Strategic, intellectually curious thinker with focus on outcomes
  • Professional image with the ability to form relationships across functions
  • Ability to train more junior analysts regarding day-to-day activities, as necessary
  • Proven ability to lead cross-functional efforts
  • Willingness and ability to learn new technologies on the job
  • Financial Services background


Exempt Status: (Yes = not eligible for overtime pay) (No = eligible for overtime pay)

Yes

Workplace Type:

Office

Our Approach to Office Workplace Type

Certain positions outside our branch network may be eligible for a flexible work arrangement. We’re combining the best of both worlds:  in-office and work from home. Our approach enables our teams to deepen connections, maintain a strong community, and do their best work. Remote roles will also have the opportunity to come together in our offices for moments that matter. Specific work arrangements will be provided by the hiring team.

Compensation Range:

Total Base Pay Range 93,000.00 - 189,000.00 USD Annual

The compensation range represents the anticipated low and high end of the base compensation range for this position. Actual compensation will vary based on various factors including but not limited to location, experience, and education.  Colleagues in this position are also eligible to participate in an applicable incentive compensation plan.  In addition, Huntington provides a variety of benefits to colleagues, including health insurance coverage, wellness program, life and disability insurance, retirement savings plan, paid leave programs, paid holidays and paid time off (PTO). 

Huntington is an Equal Opportunity Employer.

Tobacco-Free Hiring Practice: Visit Huntington's Career Web Site for more details.

Note to Agency Recruiters:  Huntington will not pay a fee for any placement resulting from the receipt of an unsolicited resume.  All unsolicited resumes sent to any Huntington colleagues, directly or indirectly, will be considered Huntington property. Recruiting agencies must have a valid, written and fully executed Master Service Agreement and Statement of Work for consideration.