Integration International Inc.
Senior Machine Learning Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Senior Machine Learning Engineer on a 10-month W2 contract, hybrid location. Key requirements include 4+ years in MLOps, strong AWS experience (SageMaker, Lambda), Python proficiency, and expertise in building APIs and CI/CD pipelines.
🌎 - Country
United States
💱 - Currency
$ USD
-
💰 - Day rate
Unknown
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🗓️ - Date
February 11, 2026
🕒 - Duration
More than 6 months
-
🏝️ - Location
Hybrid
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📄 - Contract
W2 Contractor
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🔒 - Security
Unknown
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📍 - Location detailed
Burbank, CA
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🧠 - Skills detailed
#Infrastructure as Code (IaC) #Python #AWS (Amazon Web Services) #Data Science #IAM (Identity and Access Management) #Scala #Cloud #Lambda (AWS Lambda) #Monitoring #AWS SageMaker #Data Engineering #S3 (Amazon Simple Storage Service) #ML (Machine Learning) #SageMaker
Role description
Job Title: Senior Machine Learning Engineer
Duration: 10 Months
Mode: Hybrid
Type: Contract (Only W2)
🔹 What You’ll Do
• Design, build, and operate end-to-end MLOps infrastructure on AWS
• Build core MLOps capabilities including model registries, feature stores, monitoring, and governance
• Implement infrastructure as code and scalable AWS-native solutions
• Partner closely with Data Scientists, Data Engineers, Architects, and Product teams to translate experimental models into robust production services
🔹 What We’re Looking For
• 4+ years of experience in MLOps, ML Engineering, or backend engineering with ML systems
• Strong hands-on experience with AWS (SageMaker, ECS, Lambda, Step Functions, S3, IAM)
• Proficiency in Python and experience supporting ML frameworks in production
• Experience building APIs, CI/CD pipelines, and cloud infrastructure for ML workloads
Job Title: Senior Machine Learning Engineer
Duration: 10 Months
Mode: Hybrid
Type: Contract (Only W2)
🔹 What You’ll Do
• Design, build, and operate end-to-end MLOps infrastructure on AWS
• Build core MLOps capabilities including model registries, feature stores, monitoring, and governance
• Implement infrastructure as code and scalable AWS-native solutions
• Partner closely with Data Scientists, Data Engineers, Architects, and Product teams to translate experimental models into robust production services
🔹 What We’re Looking For
• 4+ years of experience in MLOps, ML Engineering, or backend engineering with ML systems
• Strong hands-on experience with AWS (SageMaker, ECS, Lambda, Step Functions, S3, IAM)
• Proficiency in Python and experience supporting ML frameworks in production
• Experience building APIs, CI/CD pipelines, and cloud infrastructure for ML workloads
