• Interacting with customers directly to understand the business challenges, deliver briefing and deep dive sessions to customers and guide them on adoption patterns and paths to production

• Creating and delivering best practice recommendations, tutorials, blog posts, publications, sample code, and presentations tailored to technical, business, and executive stakeholders

• Providing customer and market feedback to product and engineering teams to help define product direction

Qualifications:

Required:

• Bachelor's degree or above in computer science, mathematics, statistics, machine learning or equivalent quantitative field

• 3+ years of building machine learning models for business application experience including predictive modelling, natural language processing, and deep learning

• 3+ years of hands-on experience with training, fine-tuning, evaluating, and deploying transformer models in production

• 2+ years of experience with cloud services related to machine learning (e.g., Amazon SageMaker) and coding with Python or R, using modern machine learning libraries and tools such as scikit-learn, TensorFlow, PyTorch.

• Experience with technical customer-facing engagements

Preferred:

• PhD in computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field

• AWS experience preferred, with proficiency in a range of AWS services (e.g., SageMaker, Bedrock, EC2, ECS, EKS, OpenSearch, VPC) and professional certifications (e.g., Solutions Architect Professional)

• 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

• 5+ years of deep learning, computer vision, human robotic interaction, algorithms implementation experience using PyTorch or TensorFlow

• Experience in launching AI applications in production on AWS

• 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

• Strong communication skills, with attention to detail and ability to convey rigorous technical concepts and considerations to non-experts

Company:

Launched in 2006, Amazon Web Services (AWS) began exposing key infrastructure services to businesses in the form of web services -- now widely known as cloud computing. Founded in 2002, the company is headquartered in Seattle, Washington, USA, with a team of 10001+ employees. The company is currently Late Stage. Amazon Web Services (AWS) has a track record of offering H1B sponsorships.