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Ml Inference Jobs in Indiana (NOW HIRING)

Group1001

Staff ML Engineer

Zionsville, IN

... 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 ...

Performacentric

ML Engineer

Indianapolis, IN · On-site +1

Optimize inference performance, latency, and infrastructure utilization. * Monitor model quality ... ML Engineering and MLOps practices. * LangChain, LlamaIndex, Haystack, or similar frameworks.

BC Forward

Software Engineer III (AI/ML)

Indianapolis, IN · On-site

$57 - $62/hr

Software Engineer III (AI/ML) Location: Remote - EST preferred Duration: Contract - 12 months Pay ... Hands-on experience with fine-tuning, inference, and metrics implementation for LLMs or MLLMs ...

Agilent

$183K - $286K/yr

Experience integrating AI/ML inference services and data pipelines through governed integration patterns * Background working across organizational boundaries, integrating systems owned by separate ...

New

Deloitte

AI Engineer Consultant

Indianapolis, IN · Hybrid

Deliver governed data and features for ML/GenAI (curated datasets, feature pipelines/serving) supporting training and real-time inference, including consistency, caching, backfills, and latency SLOs.

Deloitte

AI Engineer Senior Consultant

Indianapolis, IN · Hybrid

$99K - $137K/yr

Deliver governed datasets and feature engineering/serving for ML training and real-time inference (online/offline consistency, caching, latency SLOs, backfills). * Implement safety, privacy, and ...

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;

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;

INFOSYS NOVA HOLDINGS LLC

Lead Engineer

Indianapolis, IN · On-site

$97K - $129K/yr

The platform enables secure, performant, and compliant AI inference across internal enterprise ... ML engineers. Model Hosting, Fine-Tuning & Lifecycle Management · Deploy and manage fine-tuned and ...

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 ...

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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.

Which 3 jobs will survive AI?

For ML Inference roles, jobs that require complex problem-solving, creativity, and emotional intelligence are more likely to persist, such as data scientists, AI ethics specialists, and machine learning engineers. These roles involve tasks that are difficult to automate and often require specialized skills, domain knowledge, and critical thinking. Continuous learning and expertise in AI tools and programming languages like Python or TensorFlow can also enhance job security in this field.

What engineers make $500,000?

Senior machine learning engineers with extensive experience, specialized skills in deep learning, and strong industry demand can earn $500,000 or more annually, especially in high-cost-of-living areas or within top tech companies. Achieving this level typically requires advanced degrees, certifications, and a proven track record of impactful projects.

What is a $900,000 AI job?

A $900,000 AI job typically refers to high-level roles in artificial intelligence, such as senior machine learning engineers or AI research directors, often requiring advanced skills in deep learning, data science, and experience with tools like TensorFlow or PyTorch. These positions usually involve leadership responsibilities, strategic planning, and may require multiple years of specialized experience or advanced degrees.

Is ML a high paying job?

Machine Learning (ML) inference roles are generally well-paid due to the specialized skills required, such as knowledge of algorithms, programming, and data analysis. Salaries vary based on experience, location, and industry, but they tend to be higher than average for tech positions. Advanced roles often require proficiency with tools like TensorFlow or PyTorch and may include certifications or advanced degrees.

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, and why are they important?

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.
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What cities in Indiana are hiring for Ml Inference jobs? Cities in Indiana with the most Ml Inference job openings:
Ml Inference jobs near you

Senior ML Engineer

Tao Digital Solutions, Inc.

Indianapolis, IN • On-site

$99K - $137K/yr

Full-time

Posted 21 days ago

Job description

We are building a small, high-impact team to support advanced analytics and machine learning initiatives for a leading healthcare technology management company. This team will focus on predictive modeling, cost optimization, and ROI analysis for clinical asset management and capital planning.
Key Responsibilities
  • Develop and deploy statistical and machine learning models for predictive maintenance, resource optimization, and operational efficiency.
  • Perform econometric and financial analysis to support capital planning and cost-benefit decisions.
  • Design and implement data pipelines for large-scale healthcare datasets (EHR, claims, RTLS, device telemetry).
  • Collaborate with cross-functional teams (clinical engineering, finance, IT) to translate insights into actionable strategies.
  • Ensure compliance with HIPAA and healthcare data governance standards.

Required Qualifications
  • Master's or Ph.D. in Statistics, Economics, Data Science, or related field.
  • 3+ years of experience in ML model development and deployment.
  • Strong foundation in statistical inference, econometrics, and causal analysis (e.g., regression, Bayesian methods, DiD).
  • Proficiency in Python, SQL, and ML frameworks (Scikit-Learn, XGBoost, TensorFlow).
  • Excellent communication skills for presenting insights to technical and business stakeholders.

Preferred Skills
  • Experience with healthcare data (EHR, claims, RTLS).
  • Familiarity with capital planning and ROI modeling.
  • Knowledge of cloud platforms (AWS, Azure) and containerized deployments.

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