ECS ยท 2 months ago
AI Integration Engineer
Arlington, VA, 22134, United States
Full-time
Onsite
Mid LevelECS is seeking an AI Integration Engineer to work in our Arlington, VA office. This role involves leading the deployment, monitoring, and optimization of artificial intelligence and machine learning models in production environments, focusing on building scalable infrastructure and automated pipelines.
E-Commerce
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
Required
Highly skilled AI Integration Engineer
Lead seamless deployment, monitoring, and optimization of artificial intelligence and machine learning models in production environments
Design, implement, and maintain end-to-end machine learning pipelines
Automate deployment and monitoring processes while ensuring performance, observability, and security
Build scalable infrastructure, real-time dashboards, and automated pipelines that support secure, compliant, and efficient AI operations
Deploy and manage AI/ML models in production using frameworks such as MLflow, Kubeflow, or AWS SageMaker
Ensure scalability, low latency, and fault tolerance
Develop and maintain dashboards using Grafana, Prometheus, or Kibana
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
Identify data distribution shifts and trigger model retraining or alerts
Configure centralized logging systems with ELK Stack or OpenTelemetry
Capture inference events, anomalies, and audit trails for debugging, observability, and compliance
Design and manage CI/CD pipelines using GitHub Actions or Jenkins
Automate model updates, testing, and deployment workflows
Apply secure-by-design principles to protect AI pipelines and data flows
Work closely with data scientists, AI engineers, and DevOps teams
Enhance model efficiency through quantization, pruning, and performance tuning
Maximize resource utilization across hybrid and cloud platforms (AWS, Azure, Google Cloud)
Develop and maintain detailed documentation of deployment pipelines, dashboards, and monitoring procedures
Company
ECS
ECS, started its operations way back in 1954, from the famous Anarkali Bazar of Lahore.
Founded in 1954
1001-5000 employees
Funding
Current Stage
Late StageCompany data provided by crunchbase