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ECS · 3 days ago

AI Integration Engineer

Arlington, VA

Full-time

Onsite

Senior Level

5+ years exp

ECS is a leading mid-sized provider of technology services to the United States Federal Government, focused on delivering technical talent to support federal agencies. They are seeking a highly skilled AI Integration Engineer to lead the deployment, monitoring, and optimization of AI and machine learning models in production environments, ensuring performance, observability, and security.

Artificial Intelligence (AI)Cloud InfrastructureComplianceConsultingCyber SecurityInformation TechnologyMachine LearningSecuritySoftware

No H1B

Security Clearance Required

U.S. Citizen Only

Responsibilities

Deploy and manage AI/ML models in production using frameworks such as MLflow, Kubeflow, or AWS SageMaker, ensuring scalability, low latency, and fault tolerance

Develop and maintain dashboards using Grafana, Prometheus, or Kibana to provide real-time and historical visibility into model health, including accuracy, latency, and performance metrics

Implement and maintain drift detection pipelines with tools like Evidently AI or Alibi Detect to identify data distribution shifts and trigger model retraining or alerts

Configure centralized logging systems with ELK Stack or OpenTelemetry to capture inference events, anomalies, and audit trails for debugging, observability, and compliance

Design and manage CI/CD pipelines using GitHub Actions or Jenkins to automate model updates, testing, and deployment workflows

Apply secure-by-design principles to protect AI pipelines and data flows through encryption, access control, and adherence to regulations such as GDPR, HIPAA, and NIST AI RMF

Work closely with data scientists, AI engineers, and DevOps teams to align model design, deployment performance, and infrastructure optimization

Enhance model efficiency through quantization, pruning, and performance tuning to maximize resource utilization across hybrid and cloud platforms (AWS, Azure, Google Cloud)

Develop and maintain detailed documentation of deployment pipelines, dashboards, and monitoring procedures to ensure cross-team transparency and continuity

Qualification

MLOpsAI/ML deploymentPythonCI/CD automationContainerizationMonitoring toolsDrift detectionStatistical methodsSQLJavaScriptGoCollaborationProblem-solvingAttention to detailDocumentationCommunication

Required

Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related technical field

Minimum 5+ years of experience in MLOps, DevOps, or software engineering with a focus on AI/ML systems

Proven experience deploying models in production environments using MLflow, Kubeflow, or cloud AI platforms (AWS SageMaker, Azure ML, or Google Cloud Vertex AI)

Hands-on experience with observability and monitoring tools such as Prometheus, Grafana, or Datadog

Proficiency in Python and SQL; familiarity with JavaScript or Go is advantageous

Expertise in containerization and orchestration (Docker, Kubernetes) and CI/CD automation (GitHub Actions, Jenkins)

Experience with time-series databases (InfluxDB, TimescaleDB) and logging frameworks (ELK Stack, OpenTelemetry)

Familiarity with drift detection tools (Evidently AI, Alibi Detect) and data visualization libraries (Plotly, Seaborn)

Strong understanding of model evaluation metrics (e.g., precision, recall, AUC) and statistical drift detection methods (e.g., KS test, PSI)

Awareness of AI security threats (e.g., data poisoning, adversarial attacks) and mitigation using frameworks such as the Adversarial Robustness Toolbox (ART)

Proven problem-solving and debugging skills for resolving pipeline or deployment issues

Excellent collaboration and communication skills with cross-functional teams and stakeholders

High attention to detail for ensuring accuracy, traceability, and compliance in dashboard reporting and pipeline documentation

Must be a U.S. Citizen and eligible to obtain a Department of Homeland Security (DHS) EOD clearance (requires a favorable background investigation)

Preferred

Experience monitoring large language model (LLM) applications using tools such as LangSmith, Helicone, or equivalent observability platforms

Knowledge of compliance frameworks such as GDPR, HIPAA, and NIST AI RMF for secure data management and ethical AI operations

Familiarity with federated learning, edge AI, or distributed model deployment architectures

Active engagement in the MLOps or AIOps community (e.g., open-source contributions or discussions on #MLOps, #AIOps)

Professional certifications such as AWS Certified Machine Learning - Specialty, Azure AI Engineer Associate, or Google Professional Machine Learning Engineer

Professional certifications such as AWS Certified Security - Specialty, Azure Security Engineer, CompTIA Security+, or Certified Ethical Hacker (CEH)

Company

ECS

Glassdoor3.6

ECS is a fast-growing 4,000-person, $1.2B provider of advanced technology solutions for federal civilian, defense, intelligence, and commercial customers.

Founded in 2001

Fairfax, Virginia, USA

1001-5000 employees