AI/ML Engineer II
Tasks
- Build and maintain MLOps pipelines for training deployment monitoring and lifecycle management
- Collaborate with data engineering and QA to ensure auditable workflows
- Containerize and orchestrate AI services
- Continuously improve scalability reliability and efficiency of AI systems
- Deploy AI applications to cloud environments
- Design develop fine tune and evaluate machine learning models
- Document AI ML workflows architectures and learnings
- Engineer preprocess and augment training data
- Handle transform and validate structured and unstructured datasets
- Integrate AI capabilities into customer products and internal platforms
- Monitor production model performance and detect drift
- Optimize model performance for accuracy latency scalability and cost
- Perform model evaluation validation and benchmarking
- Translate product requirements into technical architectures and model designs
- Troubleshoot AI ML issues across development and production environments
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Azure | Containerization | Data Preprocessing | Deep learning | Deployment | Docker | Feature Engineering | GCP | Generative AI | Kubernetes | Language Models | Language Processing | Large Language Models | MLOps | Machine Learning | Model Validation | Monitoring | NLP | Natural Language | Natural Language Processing | Orchestration | Python | SQL
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
AI | AI/ML Engineer | Engineer | Learning Engineer | ML Engineer | Machine Learning Engineer
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