AI/ML Computational Science Manager

Role Summary

Key Responsibilities

• 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.

Required Qualifications

• 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.

Preferred Qualifications

• 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.

Education

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