AI Optimization Engineer - Hybrid Onsite 3 days
About The Position
The AI Optimization Engineer supports large-scale AI/ML and Generative AI workloads in an enterprise environment. The role focuses on optimizing, deploying, and managing machine learning models and large language models (LLMs) on GPU-accelerated HPC infrastructure. The engineer works with AI, infrastructure, and DevOps teams to build efficient model training and inference pipelines, orchestrate workloads with SLURM, and deploy containerized ML solutions in production. The position involves optimizing model performance through pruning, quantization, and knowledge distillation; managing inference workflows with Triton Inference Server and TRTLLM; and monitoring performance with Prometheus and Grafana. It requires hands-on experience with HPC environments, GPU clusters, containerization, and Linux system administration, as well as knowledge of machine learning algorithms, deep learning architectures, and modern AI development tools. Cloud platforms, vector embedding, and enterprise-scale AI deployments are preferred.
Requirements
- Strong experience in Python-based machine learning and deep learning, including NumPy, scikit-learn, TensorFlow, PyTorch, and Keras.
- Hands-on knowledge of supervised and unsupervised learning, neural networks, transformer-based models, NLP, CNNs, and Generative AI concepts.
- Expertise in HPC environments, GPU clusters, SLURM workload management, Triton Inference Server, TRTLLM, and model optimization techniques for scalable LLM deployment.
- Experience with DevOps and deployment tools including Docker, Kubernetes, MLFlow, Terraform, Jenkins, GitHub, and HuggingFace.
- Strong skills in performance monitoring using Prometheus and Grafana.
- Flask API development experience.
- Linux administration experience, including RHEL/CentOS.
- Experience with container runtimes such as Enroot, Pyxis, and Podman.
- Experience with data analysis and visualization tools such as Plotly, Seaborn, and Matplotlib.
Nice To Haves
- Experience with cloud platforms.
- Experience with vector embedding.
- Experience with enterprise-scale AI deployments.
Responsibilities
- Design and optimize AI/ML workloads on GPU-based HPC clusters.
- Deploy and manage large language models (LLMs) in scalable production environments.
- Implement model optimization techniques, including pruning, quantization, and knowledge distillation.
- Develop and manage automated job scheduling using SLURM with REST and Flask APIs.
- Deploy ML models using containerized microservices architectures.
- Monitor system performance using Prometheus and Grafana.
- Optimize inference pipelines using Triton Inference Server and TRTLLM.
- Conduct exploratory data analysis and model performance evaluation.
- Collaborate with infrastructure and ML teams to improve scalability and efficiency.
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What This Job Offers
Job Type
Full-time
Education Level
No Education Listed
