Artificial Intelligence Integration Engineer

ENTRION HOLDING CORPORATION
Arlington, VA, United States
2 months ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
5 years minimum
Working hours
Regular working hours
Job source

Tech stack

JavaScript (Programming Language) Artificial Intelligence Amazon Web Services Application Integration Architecture Audit Trail Microsoft Azure Cloud Computing Databases Continuous Integration Data Security Software Debugging DevOps
+28 more
Github Monitoring of Systems Python (Programming Language) Machine Learning Open Source Technology Prometheus Azure Machine Learning Software Deployment Software Engineering SQL Databases Datadog Data Logging Google Cloud Delivery Pipeline Large Language Models Grafana Containerization Kubernetes Information Technology Low Latency Influxdb Plotly Machine Learning Operations Kibana Data Pipelines Docker Elk Stack Jenkins

Job description

The AI Intelligence 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 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.

Requirements

Do you have experience in Software deployment?, o Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field. o Experience: o 5+ years in MLOps, DevOps, or software engineering with a focus on AI/ML systems. o Proven experience deploying models in production using MLflow, Kubeflow, or cloud platforms (AWS SageMaker, Azure ML). o Hands-on experience with observability tools like Prometheus, Grafana, or Datadog for real-time monitoring. o Technical Skills: o Proficiency in Python and SQL; familiarity with JavaScript or Go is a plus. o Expertise in containerization (Docker, Kubernetes) and CI/CD tools (GitHub Actions, Jenkins). o Knowledge of time-series databases (e.g., InfluxDB, TimescaleDB) and logging frameworks (e.g., ELK Stack, OpenTelemetry). o Experience with drift detection tools (e.g., Evidently AI, Alibi Detect) and visualization libraries (e.g., Plotly, Seaborn). o AI-Specific Skills: o Understanding of model performance metrics (e.g., precision, recall, AUC) and drift detection methods (e.g., KS test, PSI). o Familiarity with AI vulnerabilities (e.g., data poisoning, adversarial attacks) and mitigation tools like Adversarial Robustness Toolbox (ART). o Soft Skills: o Strong problem-solving and debugging skills for resolving pipeline and monitoring issues. o Excellent collaboration and communication skills to work with cross-functional teams. o Attention to detail for ensuring accurate and secure dashboard reporting. + o Must be eligible to obtain a Department of Homeland Security EOD clearance ( Requirements 1. US Citizenship, 2. Favorable Background Investigation), o Experience with LLM monitoring tools like LangSmith or Helicone for generative AI applications. o Knowledge of compliance frameworks (e.g., GDPR, HIPAA) for secure data handling. o Contributions to open-source MLOps projects or familiarity with X platform discussions on #MLOps or #AIOps.

About the company

Formed through the strategic union of Sev1Tech and ERT, Entarian is a premier provider of mission-critical engineering and technology solutions. Founded on a legacy of excellence dating back to 1993, Entarian is a product of an evolved and fully diversified engineering and federal technology leader. From deep space to defense and civilian missions, Entarian delivers secure, mission-aligned digital solutions that drive national resilience and operational effectiveness. We don’t just support modernization; we define it.

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on indeed.com

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

2:28 min

Identifying root causes through global and local SHAP plots

Bernhard Bernhard +1 · WWC 2025

2:07 min

Inspecting default bridge architectures and custom Docker networks

Oliver Seitz Oliver Seitz · WWC 2025

3:21 min

Deploying a primary Elasticsearch and Kibana cluster configuration

Philipp Krenn · WWC 2022

2:08 min

Essential engineering roles in the generative AI space

Mary Grygleski Mary Grygleski · LIVE

4:51 min

Executing simple full-text search queries using the Kibana interface

Derek Binkley · LIVE

2:34 min

Docker sandbox architecture and microVM environment integration

Manuel de la Peña Manuel de la Peña · WWC Europe 2026

Videos

See all

Related articles

See all