AI/ML Computational Science Manager

Design and develop AI and ML systems using existing cloud AI services and platforms. Architect and build scalable data pipelines for model training and production, applying DevOps and MLOps best practices. Customize and apply Deep Learning and Generative AI models to business use cases, based on data availability and system and infrastructure requirements, including edge devices and HPCs. Evaluate and justify the quality, performance, and business value of AI solutions to stakeholders. Lead research and development of new AI and high-performance compute algorithms, models, and simulations, and apply them to complex business problems. Work with large-scale datasets and apply data preprocessing techniques to ensure high-quality input for training and production. Implement and maintain efficient data storage and retrieval mechanisms for models and knowledge bases using appropriate tools. Lead, mentor, and develop a team of AI/ML engineers and data scientists, setting technical direction and engineering standards. Partner with clients and business stakeholders to identify AI opportunities, shape solutions, and drive delivery from concept to production. Desired Candidate Profile 10+ years of experience in AI/ML engineering, data science, or software engineering, including proven experience leading technical teams. Hands-on experience with at least one major cloud AI platform (e.g., Azure AI / Azure ML, AWS SageMaker / Bedrock, Google Vertex AI). Strong programming skills in Python and deep learning frameworks such as PyTorch or TensorFlow. Solid expertise in Deep Learning and Generative AI, including LLMs, fine-tuning, Retrieval-Augmented Generation (RAG), and prompt engineering. Strong MLOps and DevOps

Experience

CI/CD, Docker, Kubernetes, MLflow or Kubeflow, and model monitoring in production. Experience building large-scale data pipelines with tools such as Spark, Databricks, Airflow, or Kafka. Experience with data storage and retrieval technologies, including SQL/NoSQL databases, data lakes, and vector databases (e.g., FAISS, Milvus, Pinecone). Experience optimizing and deploying models on edge devices (e.g., ONNX, TensorRT, quantization) and/or HPC environments (e.g., CUDA, distributed training). Ability to communicate technical solutions and their business value clearly to senior stakeholders. Master's or PhD in Computer Science, Artificial Intelligence, Data Science, Applied Mathematics, or a related field. Track record in AI or HPC research, such as publications, patents, or simulation work. Relevant cloud AI certifications (Azure, AWS, or Google Cloud). Consulting or client-facing delivery experience.