MLOps Engineer
On-siteArlington, Virginia, United States
Job Summary
Deploy and manage machine learning models in production using MLflow, Kubeflow, or AWS SageMaker while ensuring scalability and low latency. Build and maintain dashboards with Grafana, Prometheus, or Kibana to track real-time model health and historical trends, and implement drift detection pipelines to identify data distribution shifts. Set up centralized logging with the ELK Stack or OpenTelemetry to capture inference events and audit trails, then develop CI/CD pipelines with GitHub Actions or Jenkins to automate model updates and testing. Apply secure-by-design principles to protect data pipelines and ensure compliance with regulations like GDPR or NIST AI RMF. Optimize models for production via quantization or pruning to ensure efficient resource usage on cloud platforms. Collaborate with data scientists, AI Integration Engineers, and DevOps teams to align model performance with business requirements. Maintain clear documentation of pipelines and monitoring processes for cross-team transparency.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field
- 5+ years in MLOps, DevOps, or software engineering with a focus on AI/ML systems
- Proven experience deploying models in production using MLflow, Kubeflow, or cloud platforms (AWS SageMaker, Azure ML)
- Hands-on experience with observability tools like Prometheus, Grafana, or Datadog for real-time monitoring
- Proficiency in Python and SQL
- Expertise in containerization (Docker, Kubernetes) and CI/CD tools (GitHub Actions, Jenkins)
- Knowledge of time-series databases (e.g., InfluxDB, TimescaleDB) and logging frameworks (e.g., ELK Stack, OpenTelemetry)
- Experience with drift detection tools (e.g., Evidently AI, Alibi Detect) and visualization libraries (e.g., Plotly, Seaborn)
- Understanding of model performance metrics (e.g., precision, recall, AUC) and drift detection methods (e.g., KS test, PSI)
- Familiarity with AI vulnerabilities (e.g., data poisoning, adversarial attacks) and mitigation tools like Adversarial Robustness Toolbox (ART)
- Strong problem-solving and debugging skills for resolving pipeline and monitoring issues
- Excellent collaboration and communication skills to work with cross-functional teams
- Attention to detail for ensuring accurate and secure dashboard reporting
- Must be eligible to obtain a Department of Homeland Security EOD clearance ( Requirements 1. US Citizenship, 2. Favorable Background Investigation)
Desired Qualifications
- familiarity with JavaScript or Go
- Experience with LLM monitoring tools like LangSmith or Helicone for generative AI applications
- Knowledge of compliance frameworks (e.g., GDPR, HIPAA) for secure data handling
- Contributions to open-source MLOps projects or familiarity with X platform discussions on #MLOps or #AIOps
Additional Requirements
- Must be eligible to obtain a Department of Homeland Security EOD clearance (Requirements: 1. US Citizenship, 2. Favorable Background Investigation)
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