MLOps Engineer

Sev1tech, Inc.

US-VA-Arlington

Job :

Type:
Full Time W/Benefits Ret Match

# of Openings: 1

Category:
Information Technology

Arlington, VA

Job Summary. We are seeking a skilled MLOps Engineer to join our team and ensure the seamless deployment, monitoring, and optimization of AI models in production.

The MLOps Engineer will design, implement, and maintain end-to-end machine learning pipelines, focusing on automating model deployment, monitoring model health, detecting data drift, and managing AI-related logging. This role will involve building scalable infrastructure and dashboards for real-time and historical insights, ensuring models are secure, performant, and aligned with business needs.

• Model Deployment: Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS Sage Maker, ensuring scalability and low latency.
• Monitoring and Observability: Build and maintain dashboards using Grafana, Prometheus, or Kibana to track real-time model health (e.g., accuracy, latency) and historical trends.
• Data Drift Detection: Implement drift detection pipelines using tools like Evidently AI or Alibi Detect to identify shifts in data distributions and trigger alerts or retraining.
• Logging and Tracing: Set up centralized logging with ELK Stack or Open Telemetry to capture AI inference events, errors, and audit trails for debugging and compliance.
• Pipeline Automation: Develop CI/CD pipelines with Git Hub Actions or Jenkins to automate model updates, testing, and deployment.
• Security and Compliance: Apply secure‑by‑design principles to protect data pipelines and models, using encryption, access controls, and compliance with regulations like GDPR or NIST AI RMF.
• Collaboration: Work with data scientists, AI Integration Engineers, and Dev Ops teams to align model performance with business requirements and infrastructure capabilities.
• Optimization: Optimize models for production (e.g., via quantization or pruning) and ensure efficient resource usage on cloud platforms like AWS, Azure, or Google Cloud.
• Documentation: Maintain clear documentation of pipelines, dashboards, and monitoring processes for cross‑team transparency.

• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.
• Experience:
  • 5+ years in MLOps, Dev Ops, or software engineering with a focus on AI/ML systems.
  • Proven experience deploying models in production using MLflow, Kubeflow, or cloud platforms (AWS Sage Maker, Azure ML).
  • Hands‑on experience with observability tools like Prometheus, Grafana, or Datadog for real‑time monitoring.
• Technical
  
  Skills:
  
  
  • Proficiency in Python and SQL; familiarity with JavaScript or Go is a plus.
  • Expertise in containerization (Docker, Kubernetes) and CI/CD tools (Git Hub Actions, Jenkins).
  • Knowledge of time‑series databases (e.g., Influx
    
    DB, Timescale
    
    DB) and logging frameworks (e.g., ELK Stack, Open Telemetry).
  • Experience with drift detection tools (e.g., Evidently AI, Alibi Detect) and visualization libraries (e.g., Plotly, Seaborn).
• AI‑Specific
  
  Skills:
  
  
  • 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).
• Soft Skills:
  • 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.
• Security Clearance: Must be eligible to obtain a Department of Homeland Security EOD clearance (Requirements 1. US Citizenship, 2. Favorable Background Investigation)

• Experience with LLM monitoring tools like Lang Smith 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.