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Ml Engineer Jobs in Indiana (NOW HIRING)

innovitusa

Data Engineer

Indianapolis, IN · On-site

$109K - $131K/yr

Support AI and analytics initiatives by preparing datasets and building pipelines for ML and ... Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field. * 8+ ...

New

inKind

Data Engineer

Austin, IN

$135K - $155K/yr

Data Engineering is a key role in the development team and is responsible for building and ... Proficiency in Python and modern AI/ML tooling and experience integrating with LLM APIs (Anthropic ...

CSpring

Cloud Data Engineer

Indianapolis, IN · On-site

$109K - $131K/yr

As a Cloud Data Engineer , you will build the modern Azure data platform that powers analytics, AI/ML, and enterprise integration for the missions our clients care about most. Your work makes it ...

CSpring

Cloud Data Engineer

Indianapolis, IN

$109K - $131K/yr

As a Cloud Data Engineer , you will build the modern Azure data platform that powers analytics, AI/ML, and enterprise integration for the missions our clients care about most. Your work makes it ...

CSpring

Cloud Data Engineer

Indianapolis, IN · On-site

$109K - $131K/yr

As a Cloud Data Engineer , you will build the modern Azure data platform that powers analytics, AI/ML, and enterprise integration for the missions our clients care about most. Your work makes it ...

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All JobsMl Engineer JobsMl Engineer Jobs in Indiana

Ml Engineer information

See Indiana salary details

$31.4K

$84.9K

$135.1K

How much do ml engineer jobs pay per year?

As of Aug 6, 2026, the average yearly pay for ml engineer in Indiana is $84,863.00, according to ZipRecruiter salary data. Most workers in this role earn between $63,300.00 and $103,700.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an ML engineer?

To thrive as an ML Engineer, you need a solid background in mathematics, statistics, computer science, and experience with machine learning algorithms, often supported by a degree in a related field. Familiarity with programming languages like Python or R, ML frameworks such as TensorFlow or PyTorch, and data processing tools is typically required, with relevant certifications being a plus. Strong problem-solving, critical thinking, and communication skills help you translate complex data insights into actionable solutions and work effectively in teams. These abilities ensure accurate model development, effective deployment, and successful collaboration on data-driven projects.

Are machine learning engineers still in demand?

Machine learning engineers are currently in high demand due to the growth of AI and data-driven technologies across industries. They typically require skills in programming, data analysis, and frameworks like TensorFlow or PyTorch, and often find opportunities in tech, finance, healthcare, and other sectors investing in AI solutions.

What is an ML engineer?

ML Engineers, or Machine Learning Engineers, are professionals who design, build, and deploy machine learning models into production systems. They bridge the gap between data science and software engineering, ensuring that machine learning solutions are scalable, reliable, and efficient. ML Engineers work with large datasets, develop algorithms, and optimize models for performance. They also collaborate with data scientists, software developers, and business stakeholders to solve real-world problems using artificial intelligence.

What is the difference between Ml Engineer vs Data Scientist?

AspectML EngineerData Scientist
Required CredentialsBachelor's or Master's in CS, Data Science, or related fields; knowledge of ML frameworksBachelor's or Master's in Statistics, Data Science, or related fields; strong analytical skills
Work EnvironmentDevelops, deploys, and maintains ML models in production systemsAnalyzes data, builds models, and provides insights for decision-making
Employer & Industry UsageTech companies, startups, and enterprises deploying ML solutionsResearch institutions, tech firms, and industries relying on data analysis

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

What are some common challenges ML engineers face when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring models remain accurate over time as data changes (known as data drift), optimizing models for speed and scalability, and integrating models seamlessly with existing software systems. Additionally, maintaining model performance in real-world environments can require continuous monitoring, retraining, and close collaboration with data engineers and DevOps teams. Addressing these challenges typically involves robust testing, using automated pipelines, and staying up-to-date with the latest MLOps best practices.
What are the most commonly searched types of Ml Engineer jobs in Indiana? The most popular types of Ml Engineer jobs in Indiana are:
What are popular job titles related to Ml Engineer jobs in Indiana? For Ml Engineer jobs in Indiana, the most frequently searched job titles are:
What job categories do people searching Ml Engineer jobs in Indiana look for? The top searched job categories for Ml Engineer jobs in Indiana are:
What cities in Indiana are hiring for Ml Engineer jobs? Cities in Indiana with the most Ml Engineer job openings:
Ml Engineer jobs near you
Infographic showing various Ml Engineer job openings in Indiana as of July 2026, with employment types broken down into 92% Full Time, 5% Part Time, and 3% Contract. Highlights an 89% Physical, 4% Hybrid, and 7% Remote job distribution, with an average salary of $84,863 per year, or $40.8 per hour.

Data Engineer

innovitusa

Indianapolis, IN • On-site

$109K - $131K/yr

Contractor

This job post has expired today. Applications are no longer accepted.

Job description

Responsibilities:

  • Design, build, and maintain scalable ETL/ELT data pipelines using Python and SQL.
  • Develop and optimize data solutions in Snowflake, including data modeling, performance tuning, and automation.
  • Integrate and transform data from Oracle, APIs, AWS services, and other enterprise systems.
  • Build and manage workflow orchestration using Apache Airflow.
  • Develop cloud-native data solutions utilizing AWS services such as S3, Glue, Lambda, and ECS/EKS.
  • Ensure data quality, reliability, security, and governance across the data platform.
  • Support AI and analytics initiatives by preparing datasets and building pipelines for ML and Generative AI use cases.
  • Collaborate closely with architects, analysts, data scientists, and business stakeholders to deliver scalable data solutions.

 

Requirements:

  • Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field.
  • 8+ years of experience in Data Engineering or Data Platform development.
  • Strong hands-on expertise in:
    • Snowflake
    • SQL (Advanced)
    • Python (Advanced)
    • AWS
    • Oracle
    • Apache Airflow
  • Experience designing and supporting enterprise-scale data pipelines and data warehouses.
  • Strong understanding of data modeling, performance optimization, and cloud-based data architectures.
  • Excellent analytical, problem-solving, and communication skills.

 

Preferred Qualifications

  • Experience with Snowpark, Streams, Tasks, and Dynamic Tables.
  • Exposure to AI/ML, Generative AI, RAG architectures, or vector databases.
  • Experience with Spark, Kafka, or Databricks.
  • SnowPro and/or AWS certifications.
  • Experience in financial services or capital markets environments.