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Mlops Data Engineer Jobs in Indiana (NOW HIRING)

RR

Data Systems/Solutions Engineer

Indianapolis, IN · On-site

$109K - $131K/yr

The Engineer applies modern software engineering and data engineering practices to ensure data ... DataOps / MLOps Enablement: * Implement CI/CD practices for data and ML workflows, including ...

Guidehouse

Data Architect

Indianapolis, IN · On-site

$61 - $78.50/hr

Job Family: Data Engineering & Architecture Consulting Travel Required: Up to 25% Clearance ... Partner with AI/ML, MLOps, and analytics teams to enable productiongrade model development and ...

Deckers

... MLOps patterns in partnership with Data Science and analytics teams. We celebrate diversity--of ... Partner with Cloud Engineering and Security to ensure AWS data solutions meet security, privacy ...

Group1001

Staff ML Engineer

Zionsville, IN · On-site

Bachelor's degree in Computer Science, Data Science, Engineering, or related field * Master's degree or equivalent experience preferred Experience: * 6-10 years in ML engineering, MLOps, or platform ...

Group1001

Staff ML Engineer

Zionsville, IN · On-site

Bachelor's degree in Computer Science, Data Science, Engineering, or related field * Master's degree or equivalent experience preferred Experience: * 6-10 years in ML engineering, MLOps, or platform ...

Onebridge

AI Developer

Indianapolis, IN · On-site

Onebridge, a Marlabs Company, is a global AI and Data Analytics Consulting Firm that empowers ... Preferred : • Experience with MLOps practices, model deployment, monitoring, and version control.

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Mlops Data Engineer information

What is the difference between Mlops Data Engineer vs Data Scientist?

AspectMlops Data EngineerData Scientist
Required SkillsMachine learning deployment, cloud platforms, scripting, data pipelinesStatistical analysis, programming, data visualization, machine learning modeling
CertificationsCloud certifications, ML engineering coursesData science certifications, statistical courses
Work EnvironmentData pipelines, cloud infrastructure, ML deployment systemsData analysis, modeling, research environments
Industry UsageTech companies, AI-focused firms, cloud service providersResearch institutions, analytics firms, tech companies

The main difference between an Mlops Data Engineer and a Data Scientist lies in their focus areas. Mlops Data Engineers specialize in deploying, maintaining, and scaling machine learning models within production environments, emphasizing infrastructure and automation. Data Scientists primarily focus on analyzing data, building models, and deriving insights. Both roles require strong technical skills, but their day-to-day tasks and career paths differ significantly.

Are MLOps engineers in demand?

MLOps Data Engineers are in high demand due to the increasing adoption of machine learning and AI across industries. They are needed to develop, deploy, and maintain scalable ML systems, often requiring skills in cloud platforms, automation, and tools like Docker and Kubernetes. The role offers strong job growth prospects as organizations prioritize operationalizing AI solutions.

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

To thrive as an MLOps Data Engineer, you need a strong background in data engineering, machine learning workflows, and software development, usually supported by a degree in computer science or a related field. Expertise with cloud platforms (such as AWS, GCP, or Azure), CI/CD pipelines, containerization tools (like Docker and Kubernetes), and familiarity with orchestration frameworks are typically required, along with certifications in cloud or data engineering. Strong problem-solving abilities, collaboration, and clear communication set professionals apart in this role. These skills and qualities are critical to efficiently deploying scalable machine learning solutions and ensuring smooth collaboration between data science and engineering teams.

What are some common challenges MLOps Data Engineers face when deploying machine learning models into production?

MLOps Data Engineers often encounter challenges such as ensuring seamless integration between data pipelines and model serving infrastructure, managing consistent data quality, and automating model retraining and monitoring. Another common hurdle is maintaining scalability and reliability as data volumes grow, and efficiently collaborating with data scientists, software engineers, and DevOps teams. Addressing these challenges requires strong communication skills, familiarity with cloud platforms, and a proactive approach to troubleshooting and automation.

What are MLOps Data Engineers?

MLOps Data Engineers are professionals who blend expertise in machine learning (ML), operations (Ops), and data engineering to streamline the deployment and management of ML models in production environments. They design and maintain data pipelines, automate workflows, and ensure the scalability, reliability, and reproducibility of machine learning systems. Their role bridges the gap between data scientists and IT operations, enabling seamless integration of ML models into real-world applications.

What is the salary of data engineer in MLOps?

The salary of an MLOps Data Engineer typically ranges from $90,000 to $150,000 annually, depending on experience, location, and company size. Professionals with skills in cloud platforms, automation, and machine learning tools tend to earn higher salaries.

What engineer makes 500,000 a year?

Highly experienced senior MLOps Data Engineers with specialized skills in cloud platforms, automation, and large-scale data processing can earn salaries approaching or exceeding $500,000 annually, especially in competitive tech hubs or large organizations. Such roles often require advanced certifications, extensive experience, and expertise in tools like Kubernetes, Docker, and cloud services like AWS or Azure.

Is MLOps required for data engineers?

MLOps is increasingly important for data engineers involved in deploying and maintaining machine learning models, as it encompasses practices like automation, monitoring, and version control. While not always mandatory, knowledge of MLOps tools such as Docker, Kubernetes, and CI/CD pipelines enhances a data engineer’s ability to support scalable and reliable ML systems.
What are popular job titles related to Mlops Data Engineer jobs in Indiana? For Mlops Data Engineer jobs in Indiana, the most frequently searched job titles are:
What job categories do people searching Mlops Data Engineer jobs in Indiana look for? The top searched job categories for Mlops Data Engineer jobs in Indiana are:
What cities in Indiana are hiring for Mlops Data Engineer jobs? Cities in Indiana with the most Mlops Data Engineer job openings:
Mlops Data Engineer jobs near you
Data Systems/Solutions Engineer

Data Systems/Solutions Engineer

RR

Indianapolis, IN • On-site

$109K - $131K/yr

Full-time

Life, Retirement, PTO

Posted 19 days ago

Job description

Position Summary
The Data Systems / Solutions Engineer serves as a key technical contributor within the Regenstrief Data Services team, functioning as a full-stack DataOps/MLOps engineer supporting research and analytics initiatives. This role is responsible for designing, building, and maintaining scalable, reliable data systems and pipelines that enable high-quality data ingestion, transformation, storage, and analysis.
The position emphasizes the development of robust, secure, and reproducible data infrastructure that supports data science, analytics, and AI-driven research. The Engineer applies modern software engineering and data engineering practices to ensure data assets are accessible, well-governed, and aligned with clinical and research requirements.
This position is a hybrid position with at least one (1) to two (2) days of onsite activity based on business needs. This position is located in downtown Indianapolis IN.
Essential Duties and Responsibilities
Data Systems Engineering and Operations:
  • Design, build, and maintain data platforms, pipelines, and services that support research, analytics, and AI/ML workloads.
  • Develop and maintain scalable data architectures using modern data warehouse/lakehouse patterns.
  • Ensure data systems are reliable, performant, and designed for long-term sustainability.
  • Implement and maintain ETL/ELT workflows, data validation, and quality monitoring processes.

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, including experiment tracking and model lifecycle considerations.
  • Apply best practices for monitoring, observability, and incident response across data systems.

Cloud, Security, and Governance
  • Design and maintain cloud-based data solutions using secure and scalable architectural patterns.
  • Apply data governance, access control, and auditing practices consistent with HIPAA-aligned research environments.
  • Ensure appropriate handling of sensitive data through de-identification, access management, and compliance controls.
  • Optimize performance and cost efficiency across compute and storage resources.

Clinical and Research Data Support
  • Work with clinical and research stakeholders to translate domain requirements into technical solutions.
  • Support integration and use of clinical and biomedical data standards (e.g., EHR data, HL7/FHIR, OMOP).
  • Produce well-documented data assets and technical specifications to support reuse and transparency.

Collaboration and Project Support
  • Collaborate with data engineers, researchers, analysts, and project managers to deliver high-quality solutions.
  • Contribute to project planning, estimation, and execution.
  • Serve as a technical resource to team members and stakeholders.
  • Document systems, workflows, and architectural decisions clearly and consistently.

Continuous Learning and Innovation
  • Maintain current knowledge of emerging tools, technologies, and best practices in data engineering and AI.
  • Leverage AI-assisted development tools responsibly to improve productivity and code quality.
  • Participate in continuous improvement efforts across systems, processes, and workflows.

Knowledge, Skills, and Abilities
Technical Knowledge:
  • Proficiency in modern data engineering concepts, including:
    • Data warehouse and lakehouse architectures
    • Dimensional modeling and data transformation patterns
    • SQL and at least one general-purpose programming language (e.g., Python)
  • Experience with CI/CD pipelines and automated testing for data and ML workflows
  • Familiarity with data quality frameworks, lineage tracking, and observability tools
  • Understanding of cloud platforms, identity and access management, and security best practices
  • Knowledge of clinical and biomedical data standards and research workflows preferred

Analytical and Problem-Solving Skills
  • Ability to analyze complex technical problems and implement effective solutions
  • Strong troubleshooting skills across data ingestion, transformation, and delivery layers
  • Ability to balance reliability, performance, and cost considerations

Communication and Collaboration
  • Strong written and verbal communication skills
  • Ability to document technical concepts clearly for both technical and non-technical audiences
  • Demonstrated ability to collaborate effectively in multidisciplinary teams

Education and Experience
  • Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field required; Master's degree preferred.
  • Minimum of three (3) years of professional experience in data engineering, systems engineering, or a related technical role.
  • Demonstrated experience in:
    • Data platform or data pipeline development
    • Cloud-based data system
    • SQL and programmatic data processing
    • DataOps or MLOps practices

Performance Expectations
  • Works independently within established guidelines and best practices.
  • Produces high-quality work with minimal supervision.
  • Demonstrates sound judgment and attention to detail.
  • Contributes to continuous improvement of tools, processes, and team effectiveness.

Physical Demands
  • Ability to work standard business hours with flexibility as needed.
  • Ability to sit or stand for extended periods.
  • Ability to operate a computer and standard office equipment.
  • Ability to lift and move materials up to 20 pounds as needed.
  • Ability to travel occasionally for meetings or training.

Work Environment
  • Hybrid office and research environment.
  • Fast-paced, deadline-driven setting.
  • Requires collaboration with internal teams and external partners.
  • Regular use of computers, communication tools, and office equipment.

BENEFITS OF WORKING HERE
  • Work with a variety of diverse professionals in the healthcare industry
  • Free parking
  • Paid holidays, vacation, and sick time
  • Group Life and Voluntary Term Life insurance
  • Long-term and Short-term Disability plans
  • Employee Assistance Program (EAP)
  • Flexible Spending Account (FSA)
  • 403b Retirement Plan with gracious employer contributions
  • Fitness program
  • Pet insurance
  • Qualified employer for loan forgiveness

Please note sponsorship and/or relocation are not available for this position.
REGENSTRIEF INSTITUTE REQUIRES ALL EMPLOYEES TO RECEIVE THE INFLUENZA VACCINATION ANNUALLY UNLESS APPROVED FOR EXEMPTION.
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities
This employer is required to notify all applicants of their rights pursuant to federal employment laws.
For further information, please review the Know Your Rights notice from the Department of Labor.

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