MLOps Engineer Security Clearance

Position: MLOps Engineer with Security Clearance
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 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.

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, 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.
* Must be eligible to obtain a Department of Homeland Security EOD clearance ( Requirements 1. US Citizenship, 2. Favorable Background Investigation) Desired Qualifications

Preferred Qualifications
* 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. About Sev1

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