AWS Machine Learning Operations Engineer

03/28/2025

Contract

Active

Job Summary:

We are seeking a skilled and experienced Machine Learning Engineer to join our team and work on deploying machine learning models in the AWS cloud environment. The ideal candidate will have a strong background in Python software development, data engineering, and building data pipelines, with hands-on experience in deploying and optimizing ML models. You will collaborate closely with data scientists to fine-tune models and solve data-related challenges while utilizing AWS services like Sage Maker, Lambda, and others.

Key Responsibilities:

• Model Deployment & Optimization:
  Deploy and optimize machine learning models in the AWS cloud environment, focusing on scalability and performance.
• Data Pipeline Development:
  Design and engineer data solutions and build efficient data pipelines, ensuring smooth data flow across the system.
• Collaboration with Data Scientists:
  Work closely with data scientists to adjust and optimize ML models and queries for better performance.
• Data Latency Management:
  Address issues related to data latency and ensure minimal delays in data processing.
• Sage Maker Usage:
  Leverage AWS Sage Maker for machine learning model training, deployment, and management.
• ETL & Data Engineering:
  Utilize strong ETL skills to process, clean, and prepare data for machine learning applications.
• Automation & Dev Ops:
  Implement CI/CD and Dev Ops automation using tools such as Jenkins and Terraform to streamline development processes.
• AI/ML Projects:
  Contribute to AI, machine learning, and deep learning projects, helping scale and optimize solutions.
• Collaborative Environment:
  Actively engage in code reviews, pair programming, and contribute to a continuous learning environment within the team.

• Experience:
  
  5+ years of experience in Python software development and building robust data pipelines.
• ETL
  
  Skills:
  
  Strong skills in ETL processes, with a focus on data transformation and cleaning.
• AWS Services:
  Extensive experience deploying machine learning models in AWS, including services such as Sage Maker, Lambda, SQS, SNS, Athena, Glue, and ECR.
• ML Model Optimization:
  Proven experience tweaking and optimizing machine learning models for deployment at scale.
• CI/CD & Dev Ops:
  Hands-on experience with CI/CD pipelines and Dev Ops automation tools like Jenkins and Terraform.
• Machine Learning & AI Projects:
  Prior experience working on AI, machine learning, or deep learning projects.
• Collaboration:
  
  Desire to work in a collaborative environment, with a focus on continuous learning, code review, and pair programming.
• Data Engineering:
  Strong background in data engineering, with specific experience in ML Ops and exposure to Data Science/AI.

• Advanced ML/AI
  
  Experience:
  
  Additional experience in more advanced machine learning techniques or deep learning frameworks.
• Cloud
  
  Certifications:
  
  AWS certifications or similar certifications related to cloud and machine learning.
• Dev Ops Automation Tools:
  Familiarity with more Dev Ops tools and automation frameworks is a plus.

AWS Certified Solutions Architect – Associate or Professional