Machine Learning Engineer

Beacon Hill; Chicago (IL); On-site; USD 120,000 - 160,000; Full time

Job summary

Beacon Hill is seeking an experienced ML Ops Engineer to collaborate with data scientists, software engineers, and DevOps teams to design, build, test, and deploy scalable ML pipelines on AWS. You will monitor production ML systems and ensure reliability, performance, and security.

The role emphasizes automating data cleaning, feature engineering, model training and deployment workflows, plus documentation and cross-functional mentoring on best practices.

Qualifications

• Bachelor’s or Master’s degree in CS/Engineering or related field.
• 5-7 years of ML Ops/DevOps or related experience.
• Experience with AWS ML services and production deployments.

Responsibilities

• Collaborate with data scientists, software engineers, and DevOps teams to develop and deploy ML models.
• Build, test, and deploy ML Ops pipelines on AWS.
• Manage and monitor production ML systems to ensure reliability and scalability.
• Design automated workflows for data cleaning, feature engineering, model training, and deployment.
• Develop and maintain documentation for ML Ops processes and procedures.
• Continuously improve ML Ops pipeline performance and efficiency.
• Troubleshoot and resolve issues related to ML model performance, data quality, and infrastructure.

Skills

ML concepts

Python

Data analysis

Communication skills

Independent work

Education

Bachelor’s or Master’s degree in CS/Engineering or related field

Tools

AWS SageMaker

Lambda

Step Functions

CloudFormation

Docker

Kubernetes

Job description

Job Description – Duties:

• Collaborate with data scientists, software engineers, and DevOps teams to develop and deploy ML models
• Build, test, and deploy ML Ops pipelines on AWS
• Manage and monitor production ML systems to ensure optimal performance, reliability, and scalability
• Design and implement automated workflows for data cleaning, feature engineering, model training, and model deployment
• Develop and maintain documentation for ML Ops processes and procedures
• Continuously improve ML Ops pipeline performance and efficiency
• Troubleshoot and resolve issues related to ML model performance, data quality, and infrastructure

Requirements:

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
• Minimum of 5-7 years of experience in ML Ops, DevOps, or related roles
• Strong knowledge of AWS services and tools related to ML Ops, such as SageMaker, Step Functions, Lambda, and CloudFormation
• Hands-on experience building and deploying ML models in production using AWS
• Proficiency in Python and/or other programming languages commonly used in ML, such as R, Java, or Scala
• Familiarity with containerization technologies such as Docker and Kubernetes
• Excellent problem-solving skills and attention to detail
• Ability to work independently as well as in a team environment
• Strong communication skills and ability to explain technical concepts to non-technical stakeholders

Knowledge, Skills, Abilities and Behaviors:

• Knowledge of machine learning concepts, algorithms, and frameworks.
• Knowledge of software engineering principles and best practices, such as version control, continuous integration, and agile development methodologies.
• Strong understanding of data analysis and data manipulation techniques.
• Ability to design and implement scalable, secure, and fault-tolerant ML Ops pipelines on AWS.
• Ability to analyze and interpret data to identify patterns, trends, and anomalies, using advanced data manipulation techniques.
• Outstanding communication skills (verbal, written, visualization, and listening).
• Self-starter who can work independently as well as in a team setting.
• Hands-on technologist with the ability to help drive the strategy and mentor others.
• Giving and receiving effective feedback across all interactions.
• Interest in understanding customer perspectives to aid in the development of the right solution.
• Interest in understanding business needs to aid in developing solutions that are right for the broader organization.