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AI Optimization Engineer
Simple Software Solutions Group, Inc. Design and optimize AI/ML workloads on GPU-based HPC clusters .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and optimizing AI/ML workloads on GPU-based HPC clusters, deploying large language models in production, and implementing model optimization techniques. Proficient in performance monitoring and automated job scheduling, with strong experience in Python-based machine learning frameworks and containerized microservices architectures.
Highest-signal resume keywords
Python-Based Machine LearningHPC EnvironmentsModel Optimization TechniquesPerformance MonitoringFlask API Development
ATS Keywords
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Hard Skills
AI/ML Workload DesignModel Optimization TechniquesExploratory Data AnalysisNeural NetworksTransformer-Based ModelsSupervised LearningUnsupervised LearningDeep LearningContainerized MicroservicesAutomated Job Scheduling
Tools & Technologies
SLURMPrometheusGrafanaDockerKubernetesMLFlowTerraformJenkinsGitHubHuggingFace
Industry Keywords
GPU ClustersGenerative AINLPCNNsCloud PlatformsVector EmbeddingEnterprise-Scale AI DeploymentsLinux AdministrationRHELCentOS
Tech Stack
Tools & technologiesCloudDockerFlaskGrafanaJenkinsKerasKubernetesLinuxMicroservicesNumpyPrometheusPythonPyTorchScikit-LearnTensorflowTerraform
About the role
Key responsibilities & impact- 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
Requirements
What you’ll need- Experienced in Python-based machine learning and deep learning
- Hands-on knowledge of NumPy, scikit-learn, TensorFlow, PyTorch, and Keras
- 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 including pruning, quantization, and distillation
- Experience with Docker, Kubernetes, MLFlow, Terraform, Jenkins, GitHub, and HuggingFace
- Strong skills in performance monitoring using Prometheus and Grafana
- Flask API development experience
- Linux administration experience with RHEL/CentOS
- Experience with Enroot, Pyxis, and Podman container runtimes
- Experience with Plotly, Seaborn, and Matplotlib
- Experience with cloud platforms, vector embedding, and enterprise-scale AI deployments highly preferred