

Senior Machine Learning Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Senior Machine Learning Engineer on a 6-month contract, hybrid in Chicago, IL, offering competitive pay. Key skills include ML modeling, AWS, PyTorch, TensorFlow, and MLOps. A Master’s degree and 5+ years of relevant experience are required.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
880
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🗓️ - Date discovered
July 17, 2025
🕒 - Project duration
More than 6 months
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🏝️ - Location type
Hybrid
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📄 - Contract type
Unknown
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🔒 - Security clearance
Unknown
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📍 - Location detailed
United States
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🧠 - Skills detailed
#Data Processing #Monitoring #AI (Artificial Intelligence) #Observability #Python #Data Pipeline #Data Engineering #Data Ingestion #Deployment #Deep Learning #Computer Science #Compliance #Recommender Systems #Kubernetes #Data Science #Batch #AWS EC2 (Amazon Elastic Compute Cloud) #BERT #Spark (Apache Spark) #"ETL (Extract #Transform #Load)" #PyTorch #Forecasting #Prometheus #Agile #SQL (Structured Query Language) #ML (Machine Learning) #TensorFlow #DevOps #Model Optimization #NLP (Natural Language Processing) #Cloud #AWS (Amazon Web Services) #EC2 #Scala #Data Architecture #Logging #Docker #Terraform #Security #GitHub #SageMaker #Automation #S3 (Amazon Simple Storage Service) #PySpark #Airflow #MLflow
Role description
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Remote work Senior Machine Learning Engineer (Chicago, IL | 6-Month Contract)
Overview
We are seeking an experienced Machine Learning Engineer (Contract) to design and build algorithmic assets across a range of domains, including Personalization, Generative AI, Forecasting, and Decision Science. This hybrid position is based in Chicago, IL and is initially scoped for 6 months, with potential for extension.
This role blends deep technical expertise in modeling with infrastructure engineering, focusing on delivering scalable ML/AI systems that power impactful real-world applications. You will work cross-functionally with data science, engineering, and architecture teams and serve as both a solutions architect and hands-on implementation engineer.
Key Responsibilities
Model Development & Optimization
• Design, train, and optimize ML models including deep learning architectures, LLMs, and transformer-based models (e.g., BERT).
• Develop distributed training pipelines using PyTorch, TensorFlow, and other ML frameworks.
• Fine-tune LLMs and optimize for efficient inference using tools such as ONNX, vLLM, and Neuron compiler.
• Optimize models for hardware-specific deployment targets (e.g., GPU, AWS Inferentia/Trainium, TPU).
Infrastructure Design & AI Services Architecture
• Design and implement AI service architectures for real-time streaming and offline batch ML systems.
• Develop and maintain scalable infrastructure for model training, data ingestion, feature engineering, and model serving.
• Deploy models using various compute environments such as EC2, EKS, SageMaker, and specialized inference chips.
MLOps Platform & Pipeline Automation
• Build and maintain foundational MLOps frameworks: Feature Store, ML Observability, Governance, and automated pipelines.
• Create infrastructure-as-code workflows for training, evaluation, and deployment.
• Develop internal MLOps tooling to streamline engineering tasks for data scientists.
• Implement CI/CD pipelines for model and infrastructure deployment using tools such as Terraform, GitHub Actions, and AWS CodePipeline.
Performance, Monitoring & Collaboration
• Monitor system performance across latency, accuracy, cost, and throughput.
• Implement observability and logging tools (e.g., CloudWatch, Prometheus, MLflow) across the ML stack.
• Work closely with data engineering to ensure optimal data pipeline formats and refresh cadences.
• Collaborate with data architecture, security, and governance teams to ensure enterprise compliance and scalability.
• Provide technical guidance on modeling strategies and ML system design best practices.
Qualifications
Required
• Master’s degree in Computer Science, Software Engineering, Machine Learning, or a related field.
• 5+ years experience developing and deploying AI/ML systems in cloud environments.
• 3+ years of hands-on ML modeling and production infrastructure experience.
• Experience with ML & Deep Learning: PyTorch, TensorFlow, LLM fine-tuning, distributed training, model optimization (ONNX, vLLM).
• Experience with Cloud & Infra: AWS (EC2, EKS, S3, SageMaker), Docker, Kubernetes, Terraform/CloudFormation.
• Strong experience in Python, SQL, PySpark, and distributed data processing (e.g., Spark, Airflow).
• Familiarity with DevOps/CI-CD: GitHub Actions, CodePipeline, automated ML pipelines.
• Experience in both real-time and batch architectures at scale.
• Excellent communication and collaboration skills.
Preferred
• Experience with recommender systems, NLP applications, or real-time inference.
• Background in MLOps platform development or building feature store systems.
• Experience working in an Agile environment.
• Understanding of enterprise-grade security, governance, and compliance in ML systems.
Work Environment
• Location: Hybrid in Chicago, IL (in-office days required per project/team needs).
• Contract Length: 6 months (with potential extension based on project needs and performance).
• Travel: Minimal to none required.