Myticas's direct client based out of Phoenix, AZ is currently seeking a Machine Learning Engineer (MLOps & AWS Expertise) for a 100% Remote contract position.
Job Summary:
We are seeking a highly skilled and experienced Machine Learning Engineer with expertise in MLOps practices and hands-on experience with AWS cloud services. The ideal candidate will be responsible for designing, implementing, and maintaining scalable machine learning models, deploying them into production environments, and ensuring their seamless integration with existing infrastructure. This role requires a blend of machine learning knowledge, MLOps best practices, and AWS cloud services experience to support and enhance the entire ML lifecycle.
Key Responsibilities:
- Develop and deploy ML models: Design, implement, and maintain scalable machine learning models using state-of-the-art techniques to solve complex business problems.
- MLOps implementation: Build and optimize CI/CD pipelines for machine learning workflows, automating the deployment, monitoring, and management of models in production.
- Model lifecycle management: Implement model training, versioning, and continuous improvement practices to ensure models remain accurate and up to date.
- AWS cloud services: Leverage AWS services like SageMaker, Lambda, S3, EC2, ECS, and Step Functions to build and deploy ML solutions at scale.
- Data pipeline development: Collaborate with data engineers to create and maintain robust data pipelines for feature engineering and model training.
- Monitoring and optimization: Set up monitoring and alerting systems for model performance, drift, and degradation, and continuously optimize for cost and performance.
- Collaboration: Work closely with data scientists, software engineers, and DevOps teams to integrate machine learning models into existing products and services.
- Security and compliance: Ensure all ML solutions are secure, compliant, and meet the organization's standards for data privacy and integrity.
Required Skills and Qualifications:
- Bachelor’s or master’s degree in computer science, Data Science, Machine Learning, or a related field.
- Hands-on experience with MLOps: CI/CD pipelines, automation, containerization, orchestration, and monitoring of ML models.
- 3+ years of experience in machine learning model development and deployment.
- AWS cloud expertise, particularly in services such as SageMaker, EC2, S3, Lambda, and Step Functions.
- Strong programming skills in Python, with experience in machine learning frameworks like TensorFlow, PyTorch, or Scikit-Learn.
- Experience working with Sagemaker on Forecast models and sentiment analysis.
- Experience with containerization technologies (Docker, Kubernetes) and infrastructure-as-code (Terraform, CloudFormation) is a nice to have.
- Strong understanding of data engineering principles, including ETL processes, feature engineering, and data preprocessing.
- Knowledge of model monitoring techniques, including drift detection and model retraining.
- Excellent problem-solving and communication skills, with the ability to work collaboratively across teams.