MLOps Engineer

Arlington, United States

Overview/ Job Responsibilities:

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.

Key Responsibilities

Model Deployment: Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS SageMaker, 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 OpenTelemetry to capture AI inference events, errors, and audit trails for debugging and compliance.
Pipeline Automation: Develop CI/CD pipelines with GitHub 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 DevOps 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.

Minimum Qualifications:

Qualifications

Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.
Experience:
- 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.
Technical Skills:
- Proficiency in Python and SQL; familiarity with JavaScript or Go is a plus.
- 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).
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.

Desired Qualifications:

Preferred Qualifications

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.

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