MLOps Engineer

• This is a 100% Onsite position and 5 days a week
  
• Selected candidate must be willing to work on-site in Woodlawn, MD
  
  

• Machine Learning, ML model deployment, Python, CI/CD for ML (Jenkins, Sagemaker), Cloud Platforms (AWS) and related ML services, and data pipeline management.
  

• Ensure that ML models can be effectively developed, deployed, managed, and monitored in production environments.
  
• Productionize ML models ? integrate trained ML models with Production systems
  
• Build and manage ML pipelines ? design, build, and maintain automated pipelines including data ingestion, data preprocessing, model training, validation, and deployment utilizing CI/CD practices.
  
• Infrastructure management ? set up and manage infrastructure for ML workloads utilizing cloud platforms and containerization technologies.
  
• Monitoring and alerting ? implement monitoring systems to track performance of ML models in Production
  
• Automation ? automate various tasks within the ML workflow to improve efficiency and reproducibility
  
• Performance optimization ? identify ways to optimize the performance, efficiency, and scalability of ML models and their supporting infrastructure.
  
• All other duties as assigned or directed.
  

Requirements

• Bachelor's Degree and 12+ years' experience in Computer Science, Mathematics, Engineering or a related field.
  
• Masters or Doctorate degree may substitute for required experience
  
• Minimum 5 years of hands-on experience designing, developing, implementing and maintaining ML workflows and data pipelines
  
• Must be able to obtain and maintain a Public Trust. Contract requirement.
  
  

• Strong foundation AI, ML and LLMs including understanding of concepts, algorithms, model training and frameworks (TensorFlow, PyTorch, scikit-learn).
  
• Strong programming skills, especially Python, and relevant libraries (scikit-Learn, TensorFlow, PyTorch, NumPy, Pandas).
  
• Strong understanding of MLOps principles and experience with MLOps platforms and tools (e.g., AWS Sagemaker, MLflow, Kubeflow, DataRobot).
  
• Experience with CI/CD tools (Jenkins required), and containerization (Docker) and orchestration (Kubernetes) for managing and scaling applications.
  
• Proficiency with cloud platforms (AWS preferred) including ML services, Infrastructure as Code (CloudFormation, Terraform), compute, storage (S3, EFS), and networking.
  
• Knowledge of data engineering fundamentals including understanding of data pipelines, data storage (PostgreSQL, MySQL, MongoDB), and data processing frameworks (Apache Spark).
  
• Strong communication, collaboration, problem-solving, analytical, and critical thinking skills.
  

• Prior experience with federal or state government IT projects.
  
• Ability to design scalable, reliable, and efficient ML systems.
  
• Willingness to continuously learn new technologies and best practices.
  
• Familiarity with other programming languages such as Java and Scala.
  
• Experience with Natural Language Processing (NLP) for text and language generation.