About the Role
We are seeking an experienced Associate Data Scientist with a strong background in Artificial Intelligence (AI), Machine Learning (ML), Generative AI (GenAI), and Large Language Models (LLMs). The ideal candidate will have hands-on experience in AI model development and deployment, as well as expertise in cloud infrastructure, distributed computing, and MLOps/LLMOps to support scalable, high-performance AI applications.
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Key Responsibilities
- Develop, train, test, evaluate, and deploy AI/ML models and Generative AI applications.
- Design, implement, and optimize LLM-based solutions, including inference mechanisms and performance tuning.
- Build and manage scalable AI infrastructure using GCP, OpenShift, Kubernetes, and Docker.
- Implement MLOps and LLMOps pipelines to automate model development, deployment, monitoring, and maintenance.
- Optimize LLM inference performance through distributed computing, inference benchmarking, and scaling strategies to meet SLAs/SLOs.
- Manage GPU-based workloads, distributed processing, and autoscaling for AI applications.
- Work with NVIDIA NIM, Hugging Face, and NVIDIA SuperPOD environments, including HPC, Slurm, and Kubernetes.
- Develop AI application architectures and manage end-to-end AI workflows.
- Implement monitoring, dashboards, and performance tracking for ML/LLM workloads and applications.
- Establish and maintain CI/CD pipelines using tools such as Argo CD, Git, and Jenkins.
- Collaborate with cross-functional teams, manage project activities, and ensure the delivery of scalable AI solutions.
Requirements
- Bachelor's degree in Computer Science or a related field.
- More than 5 years of relevant professional experience in Data Science, AI, Machine Learning, or related fields.
- Strong programming skills in Python and SQL.
- Hands-on experience with cloud technologies such as GCP, OpenShift, Kubernetes, and Docker.
- Strong understanding of AI/ML model training, testing, evaluation, and deployment.
- Practical knowledge of MLOps, LLMOps, LLMs, and Generative AI.
- Understanding of LLM architecture, inference mechanics, and performance optimization.
- Experience in inference scaling, distributed computing, benchmarking, and GPU-based workloads.
- Familiarity with NVIDIA NIM, Hugging Face, and NVIDIA SuperPOD environments.
- Experience with monitoring, dashboards, AI application architecture, and end-to-end workflows.
- Knowledge of DevOps practices and CI/CD tools, including Argo CD, Git, and Jenkins.
- Excellent communication, professionalism, leadership, and project management skills.
Preferred Skills
- Experience working with large-scale AI infrastructure and distributed AI workloads.
- Strong analytical and problem-solving abilities.
- Effective communication, teamwork, and stakeholder management skills.
- Ability to lead project activities and collaborate across technical teams.
