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Software Engineer Ai Model Training Jobs in Indiana

Jj

Staff AI/ML Software Engr, Navigation

Warsaw, IN · On-site

As a Staff AI/ML Software Engineer in the Navigation R&D team, you will architect, plan and lead ... Demonstratable expertise in ML model training/optimization, local/cloud (AWS Sagemaker, Azure ...

Pwc

AI Engineer

Indianapolis, IN

$50K - $112K/yr

... software implementation to enable the AI models to be useful and scalable. As an Associate, you ... training and/or progressively responsible work experience in Engineering with AI and Machine ...

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Software Engineer Ai Model Training information

What does a software engineer AI model training do?

A Software Engineer specializing in AI Model Training is responsible for designing, developing, and optimizing machine learning models. Their work involves preparing and processing large datasets, selecting appropriate algorithms, implementing training pipelines, and evaluating model performance. They collaborate closely with data scientists and other engineers to ensure that AI models are accurate, efficient, and suitable for deployment in real-world applications. Additionally, they may help maintain infrastructure for model training and contribute to research and development of new AI techniques.

What are the key skills and qualifications needed to thrive as a software engineer AI model training?

To excel as a Software Engineer in AI Model Training, you need strong programming skills (especially in Python), a solid grasp of machine learning fundamentals, and typically a degree in computer science or a related field. Experience with frameworks like TensorFlow or PyTorch, familiarity with data processing tools, and sometimes certifications in AI or ML are highly valuable. Analytical thinking, problem-solving, and effective collaboration enhance your ability to develop and refine complex AI models. These skills ensure that AI solutions are robust, scalable, and aligned with organizational goals in a rapidly evolving technological landscape.

What are some common challenges faced by software engineers AI model training, and how can they be addressed?

Software Engineers focusing on AI model training often encounter challenges such as managing large datasets, ensuring data quality, and optimizing model performance. Addressing these issues typically involves close collaboration with data scientists, domain experts, and DevOps engineers to streamline the data pipeline and refine training processes. Staying up to date with the latest advancements in machine learning frameworks and tools can also help overcome technical hurdles. Regular code reviews and cross-functional meetings further support problem-solving and foster a productive work environment.

What is the difference between Software Engineer Ai Model Training vs Data Scientist?

AspectSoftware Engineer Ai Model TrainingData Scientist
Required CredentialsBachelor's in CS, related field; experience with ML frameworksBachelor's or higher in CS, statistics, or related field; strong analytical skills
Work EnvironmentDevelopment teams, AI labs, cloud platformsData analysis, research environments, business units
Employer & Industry UsageTech companies, AI startups, research institutionsTech firms, finance, healthcare, consulting

While both roles involve working with data and machine learning, Software Engineer Ai Model Training focuses on developing and optimizing AI models through coding and engineering practices. Data Scientists analyze data, build models, and generate insights. The roles often collaborate but differ in their core responsibilities and skill sets.

Can I get paid to train AI models?

Yes, software engineers and AI specialists can be paid to train AI models, especially in roles that involve developing, fine-tuning, and optimizing machine learning algorithms. These positions often require knowledge of programming languages like Python, experience with machine learning frameworks, and access to computational resources. Compensation varies based on experience, location, and the complexity of the models being trained.

How to become a software engineer AI model trainer?

To become a software engineer AI model trainer, you should have a strong background in computer science, programming skills in languages like Python, and experience with machine learning frameworks such as TensorFlow or PyTorch. Gaining knowledge in data preprocessing, model evaluation, and working with large datasets is essential, along with relevant certifications or advanced degrees in AI or related fields.

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The top searched job categories for Software Engineer Ai Model Training jobs in Indiana are:

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Cities in Indiana with the most Software Engineer Ai Model Training job openings:

Software Engineer Ai Model Training jobs near you

Junior Software Engineer

Indianapolis, IN • On-site

Providesure.com
See more jobs at Providesure.comexternal

Other

Posted 8 days ago

Key responsibilities

  • Develop and maintain applications powered by LLMs

  • Build and maintain RAG pipelines and vector database integrations

  • Develop and maintain ETL scripts and Superset dashboards

Job description

Indianapolis, United States | Posted on 05/26/2026

Organization Name: ProvideSure
Website: https://www.providesure.com/
FLSA Status: Non-exempt
Prepared Date: 05-21-2026
Effective Date: 05-21-2026
Hiring Manager Job Title: Junior Software Engineer
Job Level: Individual Contributor
Employment Status: Full-time Regular
Primary Location: Hybrid/Indianapolis, IN

Job Summary

Research, design, and develop Artificial Intelligence (AI) processes or specialized utility programs. Analyze business needs and develop solutions, applying principles and techniques of computer science, engineering, and mathematical analysis. May review models, suggest changes, QC and debug programming as needed. May maintain databases within an application area, working individually or coordinating IT team. Provide mentorship to other team business areas when appropriate.

Education and Experience

Bachelor's degree or equivalent experience. Preferably in Computer Science, Software Engineering, Data Science, Artificial Intelligence, or a similar field

Knowledge
  • AI/LLM Application Development
  • Agentic AI Development and Prompt Engineering
  • RAG (Retrieval-Augmented Generation) Pipeline Development and Management
  • Vector Database Management (storage, indexing, retrieval)
  • MCP (Model Context Protocol) Server Development for tooling and analytics
  • Workflow Automation Platforms (N8N and similar)
  • AI Model Training and Fine-Tuning (including frameworks like UnSLOTH)
  • Low-Code/Internal Tooling Platforms (ToolJet)
  • WordPress Site Management
  • Backend Development (APIs, relational and NoSQL databases)
  • ETL Script Development and Maintenance
  • BI and Dashboard Development (Apache Superset)
  • Data Handling, Querying, Structuring, and Pre-Processing (Pandas, NumPy, SQL)
  • SQL and NoSQL Databases (PostgreSQL, MongoDB, Snowflake)
  • Containerization and Version Control (Docker, Git/GitHub)
  • Software Development Lifecycle
  • Testing, Validation, and Performance Tuning
  • Clear and Accurate Documentation (Confluence/Jira)
Skills
  • Knowledge and ability to use LLM models
  • Ability to create and build applications using AI LLM models
  • Assist in the development in ToolJet
  • WordPress site management
  • RAG pipeline management and development
  • Vector storage/database management and development
  • Build and maintain workflows in tools like N8N
  • Creating agentic AI agents
  • MCP servers for tools to help with KPI and analytical outputs
  • Develop and maintain ETL scripts
  • Assist in the development of Superset Dashboards
  • Clear and accurate documentation
Work Context

Communication - Must be able to communicate with CTO, CEO, COO and other members of management effectively.

Role Relationships

Reports to Chief Technology Officer

Responsibility for Others

This role does not supervise others

Work Setting

The work is typically done from a personal computer, behind a desk.

Environmental Conditions

The work is done in a temperature controlled office environment.

Job Hazards

The risk of injury on the job is extremely low.

Body Positioning

The role required sitting for long periods.

Impact of Decisions

The decisions made in this role have immediate impact on the company. The decisions can be very visible at times.

Routine versus Challenging Work

The role will require being able to change priorities, solve complex problems and offer solutions to the business.

Primary Job Duties
  • Develop and maintain applications powered by LLMs
  • Build and maintain RAG pipelines and vector database integrations
  • Develop agentic AI workflows and MCP servers
  • Build and maintain automation workflows in N8N
  • Develop and maintain ETL scripts and Superset dashboards
  • Assist with backend development (APIs, databases) and ToolJet front-end work
  • Engage in the full software development lifecycle: coding, debugging, testing, deployment, and documentation
  • Document all processes clearly and accurately
Tools and Technology

Python - Pandas, NumPy, PyTorch, UnSLOTH

Apache Airflow

Apache Superset

Git/Github

The company is an Equal Opportunity Employer, drug free workplace, and complies with ADA regulations as applicable.

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