Machine Learning Engineer

Sr Machine Learning Engineer

Duration: 6+ Months Contract

Skills: AWS SageMaker, AWS Step Functions, AWS Lambda, AWS CloudFormation, Python, R, Java, Scala, Docker, Kubernetes, TensorFlow, PyTorch, scikit-learn, XGBoost, LightGBM, Hugging Face Transformers, Airflow, Kubeflow, MLflow, Git, GitHub, GitLab, DVC (Data Version Control), Apache Spark, Apache Kafka, Amazon EC2, Amazon S3, Amazon RDS, Amazon DynamoDB, Pandas, NumPy, Amazon VPC, AWS IAM, AWS KMS, Jenkins, CircleCI, Travis CI, Argo CD, Amazon Route 53, Terraform, Jupyter Notebooks, VS Code, Prometheus, Grafana, ELK Stack.

Location: Chicago, IL (Hybrid role)

Experience: 10+ yrs

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.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Sr Machine Learning Engineer

Duration: 6+ Months Contract

Skills: AWS SageMaker, AWS Step Functions, AWS Lambda, AWS CloudFormation, Python, R, Java, Scala, Docker, Kubernetes, TensorFlow, PyTorch, scikit-learn, XGBoost, LightGBM, Hugging Face Transformers, Airflow, Kubeflow, MLflow, Git, GitHub, GitLab, DVC (Data Version Control), Apache Spark, Apache Kafka, Amazon EC2, Amazon S3, Amazon RDS, Amazon DynamoDB, Pandas, NumPy, Amazon VPC, AWS IAM, AWS KMS, Jenkins, CircleCI, Travis CI, Argo CD, Amazon Route 53, Terraform, Jupyter Notebooks, VS Code, Prometheus, Grafana, ELK Stack.

Location: Chicago, IL (Hybrid role)

Experience: 10+ yrs

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.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.