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. Leading project teams and 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. Bachelor's degree in business administration, finance, economics, computer science, data science, engineering, or other related field
2. Bachelor's degree in Computer Science, Engineering, a related field, or equivalent experience
3. 5+ years of cloud architecture and solution implementation experience
4. 5+ years of development/programming/scripting language (Python/Java/Bash/Perl) experience
5. 5+ years leading technical teams and hands-on experience focused on data, software, or ML engineering, with understanding of distributed computing (e.g., data pipelines, training and inference, ML infrastructure design)
6. 5+ years developing predictive modeling, natural language processing, and deep learning, with experience in building and deploying ML models on cloud (e.g., Amazon SageMaker or similar)
Preferred
1. Experience with the AWS platform, web services, software development, or related technologies
2. Experience conveying complex technical concepts to both technical and business audiences
3. Knowledge of one or more ML Frameworks (e.g., PyTorch, TensorFlow) and ML methods including NLP models (BERT, GPT-2/3), computer vision-based models (object detection, image recognition), and text-based models (Seq2Seq, Topic modeling)
4. Knowledge of security and compliance standards including HIPAA and GDPR
5. Experience with automation (e.g., Terraform, Python), Infrastructure as Code (e.g., CloudFormation, CDK), and Containers & CI/CD Pipelines
6. Experience building ML pipelines with MLOps best practices, including: data preprocessing, model hosting, feature selection, hyperparameter tuning, distributed & GPU training, deployment, monitoring, and retraining
7. Experience with MLOps (e.g., MLFlow, Kubeflow) and orchestration (e.g., Airflow, AWS Step Functions). Experience building applications using GenAI technologies (LLMs, Vector Stores, LangChain, Prompt Engineering)