MLOps Engineer

We are urgently looking for MLOps Engineer with in-depth experience.

Work Mode: Onsite(5days a week)

Long-term project

Job Location: Woodlawn, MD

Key Required Skills: Machine Learning, Python, NoSQL and Relational Databases, DevOps, CI/CD, and Cloud Platforms (AWS, Azure) and related ML services

Job Responsibilities:

• 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

Basic Qualifications:

• Bachelor's 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.

Required Skills:

• 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, GitHub Actions, Gitlab CI/CD, etc.)
• 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.