Artificial Intelligence Integration Engineer
Role details
Job location
Tech stack
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.