Machine Learning Engineer, Wwps Proserve Delivery Team

Amazon Amazon · Big Tech · Arlington, VA · Data Science

Machine Learning Engineer role focused on designing, implementing, and scaling Generative AI solutions for enterprise customers on AWS. The role involves working directly with customers to understand their needs, select and fine-tune models, develop proof-of-concepts, and provide technical guidance throughout the project lifecycle. It emphasizes customer success and driving AI transformation.

What you'd actually do

  1. Designing and implementing complex, scalable, and secure AI/ML solutions on AWS tailored to customer needs, including selecting and fine-tuning appropriate models for specific use cases
  2. Developing and deploying machine learning models and generative AI applications that solve real-world business problems, conducting experiments and optimizing for performance at scale
  3. Collaborating with customer stakeholders to identify high-value AI/ML use cases, gather requirements, and propose effective strategies for implementing machine learning and generative AI solutions
  4. Providing technical guidance on applying AI, machine learning, and generative AI responsibly and cost-efficiently, troubleshooting throughout project delivery and ensuring adherence to best practices
  5. Acting as a trusted advisor to customers on the latest advancements in AI/ML, emerging technologies, and innovative approaches to leveraging diverse data sources for maximum business impact

Skills

Required

  • Bachelor's degree or above in Science, Technology, Engineering, or Mathematics (STEM)
  • 3+ years of machine learning/statistical modeling data analysis tools and techniques
  • Experience in professional software engineering & best practices for the full software development life cycle, including coding standards, software architectures, code reviews, source control management, continuous deployments, testing, and operational excellence
  • Current, active US Government Security Clearance of TS/SCI

Nice to have

  • Master's degree or above in Science, Technology, Engineering, or Mathematics (STEM)
  • Experience in defining and creating benchmarks for assessing GenAI model performance
  • Experience working on multi-team, cross-disciplinary projects
  • Experience applying quantitative analysis to solve business problems and making data-driven business decisions

What the JD emphasized

  • customer needs
  • business objectives
  • customer success
  • AI transformation journey
  • deep expertise
  • architecting complex, scalable, and secure machine learning solutions
  • tailored to meet the specific needs of each customer
  • greatest value for their businesses
  • select and train and fine tune the right models
  • technical or business challenges
  • assess current data infrastructure
  • implementing AI and generative AI solutions at scale
  • design and run experiments
  • research new algorithms
  • optimizing risk, profitability, and customer experience
  • AI/ML solutions on AWS tailored to customer needs
  • selecting and fine-tuning appropriate models for specific use cases
  • machine learning models and generative AI applications that solve real-world business problems
  • conducting experiments and optimizing for performance at scale
  • identify high-value AI/ML use cases
  • gather requirements
  • propose effective strategies for implementing machine learning and generative AI solutions
  • applying AI, machine learning, and generative AI responsibly and cost-efficiently
  • troubleshooting throughout project delivery
  • adherence to best practices
  • trusted advisor to customers
  • latest advancements in AI/ML
  • emerging technologies
  • innovative approaches
  • leveraging diverse data sources for maximum business impact
  • defining and creating benchmarks for assessing GenAI model performance
  • Experience working on multi-team, cross-disciplinary projects
  • Experience applying quantitative analysis to solve business problems and making data-driven business decisions

Other signals

  • customer-facing
  • design and implement AI/ML solutions
  • scale AI/ML solutions
  • generative AI
  • customer success