About the job
Are you excited about building software solutions around large, complex Machine Learning (ML) and Artificial Intelligence (AI) systems? Want to help the largest global enterprises derive business value through the adoption and automation of Generative AI (GenAI)? Excited by using massive amounts of disparate data to develop AI/ML models? Eager to learn to apply AI/ML to a diverse array of enterprise use? Thrilled to be a key part of Amazon, who has been investing in Machine Learning for decades - pioneering and shaping the world’s AI technology?
Responsibilities
1. Implementing end-to-end AI/ML and GenAI projects, from understanding business needs to data preparation, model development, deployment and monitoring
2. Designing and implementing machine learning pipelines that support high-performance, reliable, scalable, and secure ML workloads
3. Designing scalable ML solutions and operations (MLOps) using AWS services and leveraging GenAI solutions when applicable
4. Collaborating with cross-functional teams (Applied Science, DevOps, Data Engineering, Cloud Infrastructure, Applications) to prepare, analyze, and operationalize data and AI/ML models
5. Serving as a trusted advisor to customers on AI/ML and GenAI solutions and cloud architectures
6. Sharing knowledge and best practices within the organization through mentoring, training, publication, and creating reusable artifacts
7. Ensuring solutions meet industry standards and supporting customers in advancing their AI/ML, GenAI, and cloud adoption strategies
Qualifications
Minimum
1. Experience with AI/ML technologies
2. 3+ years of building machine learning and generative AI models for business application experience
3. 3+ years of customer-facing work, engaging with customer executives, technologists or partners to solve business problems with advanced technologies experience
4. Experience with cloud services related to machine learning (e.g., Amazon SageMaker) and generative AI applications
5. 3+ years of coding, data querying languages (e.g. SQL), and scripting languages (e.g. Python)
Preferred
1. Knowledge of AWS services including compute, storage, networking, security, databases, machine learning, and serverless technologies
2. Knowledge of AWS services including SageMaker, Bedrock, EC2, ECS, EKS, OpenSearch and AWS certifications
3. 2+ years of experience with design, deployment, and evaluation of AI agents and orchestration approaches; experience with open source frameworks like LangChain, LangGraph, LlamaIndex, and/ or similar tools
4. 3+ years of deep learning, computer vision, human robotic interaction, algorithms implementation experience using PyTorch or TensorFlow
5. Experience building ML pipelines with MLOps best practices, including: data preprocessing, distributed & GPU training, model deployment, monitoring, and retraining; experience with container and CI/CD pipelines
6. Domain expertise in healthcare and life sciences