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Ml Infrastructure Jobs in Indiana (NOW HIRING)

... infrastructure roadmap centered on a Lakehouse architecture that integrates structured and unstructured data, enabling both real-time operational analytics and high-scale AI/ML workloads. * AI-Ready ...

Build and maintain the infrastructure around RL training: rollout collection, data curation, reward ... Implement methods from recent ML papers quickly and turn them into production-grade systems

Build and maintain the infrastructure around RL training: rollout collection, data curation, reward ... Implement methods from recent ML papers quickly and turn them into production-grade systems

Build and maintain the infrastructure around RL training: rollout collection, data curation, reward ... Implement methods from recent ML papers quickly and turn them into production-grade systems

Build and maintain the infrastructure around RL training: rollout collection, data curation, reward ... Implement methods from recent ML papers quickly and turn them into production-grade systems

Build and maintain the infrastructure around RL training: rollout collection, data curation, reward ... Implement methods from recent ML papers quickly and turn them into production-grade systems

Build and maintain the infrastructure around RL training: rollout collection, data curation, reward ... Implement methods from recent ML papers quickly and turn them into production-grade systems

Build and maintain the infrastructure around RL training: rollout collection, data curation, reward ... Implement methods from recent ML papers quickly and turn them into production-grade systems

Build and maintain the infrastructure around RL training: rollout collection, data curation, reward ... Implement methods from recent ML papers quickly and turn them into production-grade systems

Working closely with ML researchers, frontend engineers, and product teams across sites, you'll ... You'll also contribute to the upstream training and labeling infrastructure that feeds them. This ...

Showing results 21-40

Ml Infrastructure information

What is ML infrastructure?

ML Infrastructure refers to the underlying systems, tools, and processes that enable the development, deployment, and scaling of machine learning models. This includes data storage and management, computing resources, model training and serving environments, monitoring, and automation tools. ML Infrastructure ensures that data scientists and engineers can efficiently build, test, and maintain machine learning applications in a reliable and reproducible manner. It is a crucial foundation for organizations looking to operationalize AI and machine learning solutions at scale.

What are some common challenges faced by professionals working in ML infrastructure roles?

Professionals in ML Infrastructure often encounter challenges related to scaling systems to handle large volumes of data, ensuring reliable deployment pipelines, and maintaining reproducibility across different environments. They must also collaborate closely with data scientists and engineers to streamline workflows and address issues like version control and model monitoring. Staying updated with rapidly evolving tools and best practices is essential, and balancing stability with innovation is a frequent aspect of the role.

What are the key skills and qualifications needed to thrive as an ML infrastructure engineer, and why are they important?

To thrive as an ML Infrastructure Engineer, you need a strong background in software engineering, cloud computing, and machine learning concepts, often supported by a degree in computer science or a related field. Proficiency with containerization tools (like Docker and Kubernetes), cloud platforms (such as AWS, GCP, or Azure), and CI/CD systems is critical. Excellent problem-solving, collaboration, and communication skills help you efficiently work with data scientists and DevOps teams. These skills and qualities are vital for building scalable, reliable ML systems that support rapid experimentation and deployment in production environments.

What is the difference between Ml Infrastructure vs Data Engineer?

AspectML InfrastructureData Engineer
Required CredentialsBachelor's in CS, Data Science, or related; knowledge of cloud platformsBachelor's in CS, Software Engineering, or related; experience with databases and ETL tools
Work EnvironmentFocus on deploying and maintaining ML systems, cloud environments, and infrastructure toolsDesigning, building, and managing data pipelines and storage solutions
Industry UsageUsed in AI/ML teams to support model deployment and scalabilityUsed across data-driven organizations for data management and analytics

ML Infrastructure specialists focus on deploying, scaling, and maintaining machine learning systems and infrastructure, while Data Engineers primarily build and manage data pipelines and storage solutions. Both roles require technical skills and often collaborate, but their core responsibilities differ in focus and tools used.

What are popular job titles related to Ml Infrastructure jobs in Indiana?

For Ml Infrastructure jobs in Indiana, the most frequently searched job titles are:

What job categories do people searching Ml Infrastructure jobs in Indiana look for?

The top searched job categories for Ml Infrastructure jobs in Indiana are:

Infographic showing various Ml Infrastructure job openings in Indiana as of August 2026, with employment types broken down into 91% Full Time, 7% Part Time, and 2% Contract. Highlights an 82% Physical, 6% Hybrid, and 12% Remote job distribution.

Director, Data Engineering

Michigan City, IN โ€ข On-site

$165K/yr

Full-time

Posted 27 days ago


Job description

Description:

We are seeking a visionary Director, Data Engineering to architect the "data set of the future." This role is not just about reporting; it is about building the scalable, AI-ready infrastructure that will fuel our next generation of manufacturing innovation. You will move the organization beyond traditional data warehousing to a robust Data Lakehouse architecture, ensuring our enterprise data—from shop floor to point-of-sale—is clean, real-time, and ready for advanced GenAI and predictive modeling. 

The ideal candidate is a technologist who fluently bridges the gap between the plant floor and the front office. You will be responsible for integrating complex operational data with high-velocity sales and commercial data to create a unified ecosystem. By connecting factory efficiency directly to customer demand and market trends, you will enable us to pivot from reactive operations to a truly predictive enterprise.


Key Responsibilities:

  • Architecting the Future: Define and execute a data infrastructure roadmap centered on a Lakehouse architecture that integrates structured and unstructured data, enabling both real-time operational analytics and high-scale AI/ML workloads.
  • AI-Ready Foundation: Establish the data governance, cataloging, and lineage frameworks necessary to power secure, trusted AI models and Large Language Models (LLMs) across the enterprise.
  • Manufacturing Integration: Partner with OT and Engineering teams to ingest and operationalize IIoT and supply chain data, creating a unified data ecosystem that drives predictive maintenance and factory floor efficiency.
  • Modern Data Stack Leadership: Oversee the transition from legacy BI tools to modern, self-service analytics platforms, ensuring the organization has the agility to derive insights from the data lakehouse.
  • Data Ops & Governance: Lead the transition to MLOps and DataOps methodologies, ensuring data quality, security, and compliance in an increasingly automated environment.
  • Strategic Partnership: Collaborate with business unit leaders to identify and prioritize data products that drive measurable top-line growth or operational cost reductions.
  • Team Leadership: Build and mentor a high-performing team of data engineers, ML engineers, and data architects who are comfortable in both cloud-native environments and complex legacy manufacturing systems.
Requirements:

Qualifications and Technical Requirements:

  • Strategic Experience: 15+ years in data strategy, architecture, and engineering, with at least 5 years in a leadership role driving organizational change.
  • 5+ years in a leadership role managing data & analytics teams. 
  • Architecture Expertise: Demonstrated experience designing and deploying Lakehouse architectures (e.g., Databricks, Snowflake, or similar) at scale.
  • AI/ML Fluency: Proven experience operationalizing AI/ML models within an enterprise environment; deep understanding of data preparation for LLMs and generative AI.
  • Cloud Proficiency: Extensive experience with Azure (or equivalent cloud hyperscaler) data stacks (e.g., Synapse/Fabric, ADLS Gen2, Azure AI).
  • Tooling: Advanced proficiency in Python, Spark, and SQL; strong experience with CI/CD for data pipelines and infrastructure-as-code.
  • Education: Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related technical field.
  • Soft Skills: A "product manager" mindset for data; the ability to translate complex technical architectural debt into business-friendly value proposition

Essential/Preferred Skills:

  • Experience with data governance frameworks and tools. 
  • Exposure to advanced analytics, data science, or machine learning initiatives. 
  • Experience in manufacturing, industrial, or eCommerce environments preferred.

Work Conditions and Physical Requirements:

  • Ability to work in both office and manufacturing environments. 
  • Availability to work outside of core business hours, including nights, weekends, and holidays when required for system upgrades or migrations.
  • Required to sit or stand for long periods of time. 
  • The ability to lift 30-50 lbs without assistance. 
  • Local and/or international travel will be required as needed (10-15%) including some extended stays on location for education or deployments. Must have a valid driver's license and Passport.