Responsibilities
AI Infrastructure & Environment Management
- Manage and maintain cloud environments for AI workloads
- Ensure secure and stable connectivity between enterprise systems and AI platforms
- Monitor system performance and maintain reliability of AI models in production
- Implement security controls for AI data handling and model deployment
AI Prototyping & Technical Evaluation
- Conduct technical evaluation of AI tools, frameworks, and vendor solutions
- Build proof-of-concepts to validate AI feasibility and performance
- Stress-test AI APIs, model endpoints, and third-party integrations
- Document findings and provide recommendations on AI adoption
Data Pipelines & MLOps Engineering
- Develop and maintain data pipelines for AI training and inference
- Implement MLOps practices for model deployment, monitoring, and lifecycle management
- Build and maintain model serving infrastructure and APIs
- Ensure reliable data flow between source systems and AI model
Requirements
- Degree in Computer Science, Data Science, AI/ML, or related field
- 3+ years of experience in AI/ML engineering or related technical roles
- Strong Python programming skills with ML libraries (TensorFlow, PyTorch, scikit-learn)
- Experience with cloud platforms (AWS, Azure, or GCP)
- Knowledge of MLOps, model deployment, and monitoring
- Experience with API development and integration for AI services
- Understanding of data pipelines and ETL processes
- Familiarity with government technology standards and AI governance frameworks
- Experience with Government Commercial Cloud (GCC) or similar secure environments
