About the Role
We are seeking an experienced AI/ML Engineering Manager to lead the design, development, and deployment of scalable AI and machine learning solutions. This role involves transforming business needs into production-grade AI systems utilizing cloud AI services, deep learning, and Generative AI across cloud, edge, and High Performance Computing (HPC) environments. You will also spearhead R&D into new algorithms and models to tackle complex problems.
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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, considering 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 algorithms, models, and simulations, applying them to complex business challenges.
- Work with large-scale datasets and utilize 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, establishing technical direction and engineering standards.
- Collaborate with clients and business stakeholders to identify AI opportunities, shape solutions, and ensure delivery from concept to production.
Required Qualifications
- 10+ years of experience in AI/ML engineering, data science, or software engineering, including a strong background in 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).
- Proficient programming skills in Python and familiar with 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 in building large-scale data pipelines with tools such as Spark, Databricks, Airflow, or Kafka.
- Familiarity 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.
- Proven track record in AI or HPC research, such as publications, patents, or simulation work.
- Relevant cloud AI certifications (Azure, AWS, or Google Cloud).
- Experience in consulting or client-facing delivery roles.
Education
Bachelor's degree in Computer Science, Engineering, or a related field.
