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 Python NoSQL and Relational Databases DevOps CI/CD and Cloud Platforms (AWS Azure) and related ML services.
  

• 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

• Bachelors Degree and 10 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 pipelines
  
• Must be able to obtain and maintain a Public Trust. Contract requirement.
  

• Strong foundation in Machine Learning 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 DevOps principles and experience with CI/CD tools (Jenkins Required)
  
• Proficiency with cloud platforms (AWS preferred) including ML services compute storage (S3 EFS) and networking.
  
• Experience with containerization (Docker) and orchestration (Kubernetes).
  
• Knowledge of data engineering fundamentals including understanding of data pipelines data storage (PostgreSQL MySQL MongoDB) and data processing frameworks (Apache Spark).
  
• Familiarity with MLOps platforms and tools (e.g. Sagemaker MLflow Kubeflow DataRobot).
  
• 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.