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Ml Inference Jobs in Indiana (NOW HIRING)

Group1001

Staff ML Engineer

Zionsville, IN · On-site

... and operating inference infrastructure at scale * CI/CD for ML : Building ML pipelines with ... SageMaker Pipelines, Kubeflow, Airflow, or Dagster; automated model testing, validation gates, and ...

Group1001

Staff ML Engineer

Zionsville, IN · On-site

... and operating inference infrastructure at scale * CI/CD for ML : Building ML pipelines with ... SageMaker Pipelines, Kubeflow, Airflow, or Dagster; automated model testing, validation gates, and ...

Deloitte

AI Engineer Consultant

Indianapolis, IN · Hybrid

Deliver governed datasets and feature engineering/serving for ML training and real-time inference (online/offline consistency, caching, latency SLOs, backfills). A successful candidate would possess ...

Elevance Health

Gen AI Engineer

Indianapolis, IN · Hybrid

... for inference optimization; RAG architecture design and implementation. * Advanced cloud infrastructure (AWS EKS/ECS, GCP GKE, Azure AKS) knowledge. * Containerization strategies for ML workloads;

Elevance Health

Gen AI Engineer

Indianapolis, IN · Hybrid

... for inference optimization; RAG architecture design and implementation. * Advanced cloud infrastructure (AWS EKS/ECS, GCP GKE, Azure AKS) knowledge. * Containerization strategies for ML workloads;

Cisco

Applied AI Scientist

Indianapolis, IN · On-site

Explore and evaluate new AI/ML techniques, tools, and methodologies, applying relevant innovations ... and inference efficiency to minimize cost and latency while preserving accuracy. * MLOps ...

Deloitte

Google AI Lead Architect

Indianapolis, IN

$52.75 - $72.50/hr

Integrate and fine-tune Large Language Models (LLMs) and other AI/ML models into enterprise applications. Develop and implement strategies for model deployment, inference, and monitoring, with an ...

inKind

Data Engineer

Austin, IN · On-site

$135K - $155K/yr

The position requires working across departments to build, operate, and optimize highly available data pipelines that feed analytics, ML training and inference, and retrieval-augmented generation ...

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Ml Inference information

What is ML inference?

ML inference refers to the process of using a trained machine learning model to make predictions or decisions based on new data. After a model has been trained on historical data, inference is the phase where that model is deployed and used in real-world applications, such as recognizing speech, detecting objects in images, or recommending products. The focus in ML inference is on speed, efficiency, and scalability to ensure quick predictions, often in real time. This process is critical for practical applications like mobile apps, web services, and embedded systems. Optimizing inference involves reducing latency, memory usage, and computational requirements.

What is the difference between Ml Inference vs Data Scientist?

AspectML InferenceData Scientist
Required CredentialsKnowledge of machine learning models, programming skillsDegree in data science, statistics, or related fields
Work EnvironmentDeploying models in production, real-time data processingData analysis, model development, research
Industry UsageAI product deployment, software companiesResearch institutions, tech firms, consulting

ML Inference focuses on deploying trained models to make predictions on new data, often in real-time. Data Scientists develop and analyze models, working primarily in research and development. While both roles require understanding of machine learning, ML Inference emphasizes deployment and operationalization, whereas Data Scientists focus on model creation and analysis.

What are some common challenges faced by ML inference engineers when deploying models to production?

ML Inference Engineers often encounter challenges such as optimizing model latency and throughput to meet production requirements, ensuring compatibility with diverse hardware environments, and managing model versioning and updates without disrupting service. Additionally, balancing resource utilization and inference accuracy while monitoring real-time performance metrics is crucial. Collaboration with data scientists, DevOps, and software engineers is typically essential to streamline deployment and maintain robust, scalable inference pipelines.

What are the key skills and qualifications needed to thrive in ML inference?

To thrive in ML Inference, you need a solid background in machine learning principles, programming (Python or C++), and experience with deploying models at scale, often supported by a degree in computer science or a related field. Familiarity with frameworks and tools such as TensorFlow, PyTorch, ONNX, and cloud platforms like AWS SageMaker or Google AI Platform is typically required. Strong problem-solving skills, attention to detail, and effective communication are crucial soft skills for collaborating with multidisciplinary teams and optimizing model performance. These skills ensure efficient, scalable, and reliable deployment of machine learning solutions in real-world applications.

Is ML inference a high paying job?

ML inference roles are generally well-paying, especially for those with skills in machine learning frameworks, programming, and cloud platforms. Salaries vary based on experience, location, and industry, but they tend to be higher than average for tech-related positions.
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Ml Inference jobs near you

Staff ML Engineer

Group1001

Zionsville, IN • On-site

Full-time

Medical, Dental, Vision, Life, Retirement

Re-posted 26 days ago

Group1001 rating

9.5

Company rating: 9.5 out of 10

Based on 8 frontline employees who took The Breakroom Quiz

7th of 301 rated insurance

Job description

Group 1001is a consumer-centric, technology-driven family of insurance companies on a mission to deliver outstanding value and operational performance by combining financial strength and stability with deep insurance expertise and a can-do culture. Group1001's culture emphasizes the importance of collaboration, communication, core business focus, risk management, and striving for outcomes. This goal extends to how we hire and onboard our most valuable assets - our employees.

*Please note, this position requires an in-person interview.

Why This Role Matters:

We're building AI&ML-powered products that will transform how Group 1001 approaches pricing optimization, claims automation, and risk intelligence. To do this at scale, we need robust ML infrastructure-not just great models.

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 our ML platform architecture, working alongside Platform Engineering teams to ensure ML workloads run reliably on our modern stack: Snowflake, Dagster, Coalesce, Palantir and AWS SageMaker.

This role is for engineers who are as passionate about infrastructure, deployment, and operationalizing ML as they are about the models themselves

*Please note, this position requires an in-person interview.

How You'll Contribute:

  • Partner with Data & Platform Engineering to define how ML workloads integrate with our Snowflake-Dagster-Palantir ecosystem

  • Evaluate and recommend tooling for the ML stack-balancing build vs. buy decisions against our scale and compliance needs

  • Contribute to platform roadmap discussions, advocating for infrastructure investments that accelerate ML delivery

  • Establish CI/CD pipelines for ML: automated testing, model validation, staged deployments, and rollback capabilities using SageMaker Pipelines, Step Functions, or similar orchestration

  • Implement model monitoring and observability: drift detection, performance degradation alerts, and automated retraining triggers

  • Architect ML workloads on AWS: SageMaker (Training Jobs, Processing, Endpoints), EC2/EKS for custom serving, S3 for artifact storage, and IAM for secure access patterns

  • Optimize for cost and performance-right-sizing instances, spot instance strategies, auto-scaling endpoints, and efficient GPU utilization

  • Integrate ML infrastructure with our Dagster orchestration layer for end-to-end pipeline visibility

  • Mentor senior ML engineers and technical leads, developing the next generation of ML engineering leadership

What We're Looking For:

Technical Skills:

  • MLOps & Model Serving: Hands-on experience with model serving frameworks (SageMaker Endpoints, Seldon Core, BentoML, Ray Serve, or TensorFlow Serving); building and operating inference infrastructure at scale

  • CI/CD for ML: Building ML pipelines with SageMaker Pipelines, Kubeflow, Airflow, or Dagster; automated model testing, validation gates, and deployment automation

  • AWS & Cloud Infrastructure: Strong AWS experience-SageMaker, EKS/ECS, Lambda, Step Functions, S3, IAM; infrastructure-as-code (Terraform, CDK, CloudFormation)

  • Monitoring & Observability: Model monitoring, drift detection, alerting; tools like Evidently, WhyLabs, SageMaker Model Monitor, or custom solutions

  • Core ML Fundamentals: Working knowledge of Python, ML frameworks (PyTorch, TensorFlow, scikit-learn), and model evaluation-enough to partner effectively with data scientists

  • Feature Engineering Infrastructure: Experience with feature stores (SageMaker Feature Store, Feast, Tecton, or similar); designing feature pipelines for both batch and real-time serving

  • Experiment Tracking & Registry: MLflow, Weights & Biases, SageMaker Experiments, or similar; establishing reproducibility and governance across ML projects

  • Nice to Have: Palantir Foundry, Kubernetes, Bedrock, cost optimization strategies for ML workloads

Education:

  • 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 engineering with a focus on productionizing ML systems

  • Demonstrated experience building ML infrastructure that others build upon-serving layers, feature stores, or MLOps tooling

  • Track record of improving ML delivery velocity through infrastructure and automation

  • Proven ability to work cross-functionally with data scientists, platform engineers, and stakeholders

  • Experience mentoring and developing senior engineers and technical leaders

  • Strong executive presence with ability to influence stakeholders at all levels of the organization

Preferred Qualifications:

  • Experience in insurance or financial services with deep understanding of industry challenges

  • Recognized expertise through conference presentations, publications, or industry speaking engagements

  • Experience with enterprise-scale systems and complex technical environments

  • Proven ability to build consensus and drive alignment across multiple teams and stakeholders

Competencies and Soft Skills:

  • Executive presence with ability to influence senior leadership and drive organizational change

  • Strategic vision with ability to define long-term technical direction aligned with business goals

  • Strong leadership skills with proven ability to develop and mentor senior technical talent

  • Exceptional communication skills with ability to articulate technical strategy to executive audiences

  • Political acumen with ability to navigate complex organizational dynamics and build consensus

Compensation:
Our compensation reflects the cost of labor across several U.S. geographic markets. The base pay for this position ranges from $190,000/year in our lowest geographic market up to $215,000/year in our highest geographic market. Pay is based on factors such as market location, job-related skills, and experience.

Benefits Highlights:

Employees who meet benefit eligibility guidelines and work 30 hours or more weekly, have the ability to enroll in Group 1001's benefits package. Employees (and their families) are eligible to participate in the Company's comprehensive health, dental, and vision insurance plan options. Employees are also eligible for Basic and Supplemental Life Insurance, Short and Long-Term Disability. All employees (regardless of hours worked) have immediate access to the Company's Employee Assistance Program and wellness programs-no enrollment is required. Employees may also participate in the Company's 401K plan, with matching contributions by the Company.

Group 1001, and its affiliated companies, is strongly committed to providing a supportive work environment where employee differences are valued. Diversity is an essential ingredient in making Group 1001 a welcoming place to work and is fundamental in building a high-performance team. Diversity embodies all the differences that make us unique individuals. All employees share the responsibility for maintaining a workplace culture of dignity, respect, understanding and appreciation of individual and group differences.

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