Machine Learning Ops (MLOps) Engineer

Overview

FTI is looking for an Intermediate Machine Learning Ops (MLOps) Engineer to join our Data Science and Engineering team. This role will be critical in designing infrastructure, developing process, deploying, scaling, and maintaining machine learning models while also having the ability to contribute to our data science initiatives. This role bridges the gap between data science and engineering, ensuring that machine learning models are efficiently integrated and maintained within production systems.

Responsibilities

• MLOps Infrastructure Development:
  • Design, implement, and manage scalable MLOps pipelines to automate the deployment and monitoring of machine learning models.
  • Build and maintain CI/CD pipelines for ML model integration and delivery.
  • Optimize model training and inference performance.
• Cloud and Containerization:
  • Deploy and manage ML models on cloud platforms (AWS, Azure, GCP) using containerization tools like Docker and Kubernetes.
  • Ensure high availability, scalability, and cost-efficiency of ML services.
• Model Monitoring and Maintenance:
  • Implement model monitoring systems to track model performance, data drift, and model decay.
  • Develop automated retraining pipelines triggered by data or performance metrics.
• Collaboration and Data Science Contribution:
  • Collaborate with data scientists to transition models from development to production.
  • Assist in feature engineering, model optimization, and data pipeline automation.
  • Contribute to data science discussions, providing insights on deployment feasibility and infrastructure requirements.

Education/Qualifications

• 3+ years of experience in MLOps, DevOps, or a related engineering role.
• Must be a U.S. Citizen and able to obtain a DoD clearance. 
• Proven experience with CI/CD pipelines and automation tools (e.g., Jenkins, GitLab CI, CircleCI).
• Hands-on experience with cloud platforms (AWS, Azure, GCP) and container orchestration (e.g., Kubernetes, Docker).
• Proficiency in Python and familiarity with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Strong understanding of MLOps best practices, including model versioning, monitoring, and continuous integration.
• Ability to optimize model training and inference performance.
• Familiarity with data pipeline orchestration tools (e.g., Apache Airflow, Prefect).

Preferred Qualifications:

• Experience working in secure environments or with government contracts.
• Active security clearance or eligibility to obtain one.
• Exposure to data science concepts, including feature engineering, model evaluation, and data visualization.
• Familiarity with ML lifecycle management tools such as MLflow, TFX, or Kubeflow.

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