AI Optimization Engineer

Qualifications

1. Proficiency in languages such as Python, with experience in libraries like NumPy and scikit-learn.
2. Knowledge of various machine learning algorithms, including supervised and unsupervised learning, neural networks, decision trees, clustering, and dimensionality reduction.
3. Experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras, and knowledge of their architectures and APIs.
4. Proficient with SLURM workload manager with REST and Flask APIs for automated and secure job scheduling.
5. Experienced in scalable infrastructure for deploying and managing large language models (LLMs),
6. HPC engineer with hands-on experience designing and managing GPU-accelerated clusters for large-scale AI/ML workloads.
7. Experience with deploying machine learning models in production environments, including containerization, microservices, and API design.
8. Leveraging Prometheus and Grafana to collect and analyze metrics, identify performance issues, and implement fixes. Experience creating Slurm and Triton metrics will be a plus.
9. Familiarity with Triton Inference Server, including its architecture, configuration, and deployment.
10. Knowledge of model optimization techniques, including pruning, quantization, and knowledge distillation.
11. Exploratory Data Analysis - Plotly, Seaborn, matplotlib
12. Deep Learning, Neural Networks, Decision Trees, Ensemble Methods, Gradient Boosting, Support Vector Machines, Random Forest, Logistic Regression, Transfer learning, Transformer based models, BART, Hyperparameter Tuning, Gen-AI, CNN, Computer Vision, NLP
13. Tools and Platforms like - Docker, Kubernetes, Jupyter, MLFlow, Github, Terraform, Jenkins, HuggingFace
14. Flask API Development and Security
15. Container Runtimes: Enroot, Pyxis, Podman
16. Linux (RHEL/CentOS) System Administration
17. Model Optimization techniques using Triton with TRTLLM Desired Qualifications:
18. Experience with data cleaning, feature scaling, and normalization
19. Programming skills creating UI/UX using the Angular framework, HTML, CSS, and JavaScript
20. Creating vector embeddings
21. Tools and Platforms like - AWS (SageMaker, Lambda, EC2)
22. Database Technologies Oracle, MS-SQL, MongoDB, Redis and MySQL
23. SQL and PL/SQL Scripting