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Ml Inference Jobs in Indiana (NOW HIRING)

Purple Drive Technologies

Senior AI/ML Engineer

Indianapolis, IN · On-site

$99.90K - $137.20K/yr

AI/ML Engineer: 10+ Years of Experience Skills AI/ML Strong Python Coding Experience LLM workflows ... optimize inference pipelines for latency, scalability, and cost efficiency across cloud (AWS/GCP ...

Black Forest Labs

$100.10K - $131.30K/yr

Designs, deploys, and maintains cloud-based ML training clusters (Slurm) and inference clusters (Kubernetes) that researchers and products depend on * Implements and manages network-based cloud file ...

Deloitte

AI Engineer Senior Consultant

Indianapolis, IN · Hybrid

$99.90K - $137.20K/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 ...

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 Data Engineer - Senior Consultant

Indianapolis, IN · Hybrid

$99.90K - $137.20K/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 ...

Deloitte

AI Engineer Senior Consultant

Indianapolis, IN · Hybrid

$99.90K - $137.20K/yr

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 ...

INFOSYS NOVA HOLDINGS LLC

Lead Engineer

Indianapolis, IN · On-site

$97.90K - $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 ...

S&P Global

Senior Machine Learning Engineer

Union City, IN · On-site +1

$95.20K - $130.70K/yr

Proven experience building ML pipelines for data processing, training, inference, maintenance, evaluation, versioning, and experimentation. * Demonstrated effective coding, documentation ...

Spgi

Senior Machine Learning Engineer

Union City, IN · On-site +1

$95.20K - $130.70K/yr

Proven experience building ML pipelines for data processing, training, inference, maintenance, evaluation, versioning, and experimentation. * Demonstrated effective coding, documentation ...

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Ml Inference information

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.

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 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.

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Ml Inference jobs near you

Senior AI/ML Engineer

Purple Drive Technologies

Indianapolis, IN • On-site

$99.90K - $137.20K/yr

Full-time

Posted 21 days ago

Job description

Overview:
AI/ML Engineer: 10+ Years of Experience
Skills
AI/ML
Strong Python Coding Experience
LLM workflows
(AWS/GCP/Azure)
ETL workflows using Spark, Glue, Airflow,
• Design, develop, and maintain scalable Python applications, libraries, and scripts for data pipelines, APIs, and LLM workflows, ensuring code quality and reusability.
• Craft, test, and optimize prompts for generative AI/LLM models; integrate Hugging Face transformers and fine-tuned models into ETL and downstream applications.
• Build and manage robust ETL workflows using Spark, Glue, Airflow, or similar; handle structured/unstructured data ingestion, transformation, and persistence across data lakes, warehouses, and RDS systems.
• Develop and operationalize LLM/transformer models via Hugging Face ecosystem; optimize inference pipelines for latency, scalability, and cost efficiency across cloud (AWS/GCP/Azure) and containerized environments (ECS/Fargate/Kubernetes).

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