Description

　　Do you want to help define the future of Go to Market (GTM) at AWS using generative AI? You will be part of the core worldwide GenAI Models team, responsible for helping our customers build and deploy Generative AI (GenAI) models to power end applications and use cases across industry verticals. You will engage with product owners to influence product direction and help our customers tap into new markets by utilizing GenAI along with AWS Services. AWS works with a number of GenAI model producers, you will be working directly with them around model performance evaluations, developing demos and proof-of-concepts of GenAI end applications / use cases with large models.

　　Key job responsibilities

　　You will help develop the industry’s best cloud-based solutions to grow the GenAI business. Working closely with our product and field teams and 3rd party model providers, you will help enable new capabilities for our customers to develop and deploy GenAI workloads on AWS. This involves activities like market sizing, building an opportunity pipeline, working with customers to understand technical requirements for proof-of-concepts (POC), creating content to train the field teams, and driving thought-leadership.

　　You will possess a business and technical background that enables you to drive an engagement and interact at the highest levels with startups, Enterprises, and AWS partners. You will have the technical depth and business experience to easily articulate the potential and challenges of GenAI models and applications to engineering teams and C-Level executives. This requires deep familiarity across the stack – compute infrastructure, ML frameworks, orchestration layers, large models, frameworks like LangChain for developing applications powered by GenAI models, MLOPs, as well as target use cases in the cloud.

　　You will drive the development of the GTM plan for building and scaling GenAI on AWS, interact with customers directly to understand their business problems, and help them with defining and implementing scalable GenAI solutions to solve them (often via proof-of-concepts). You will also work closely with account teams, research scientists, and product teams to drive model implementations and new solutions.

　　You should be passionate about helping companies/partners understand best practices for operating on AWS. An ideal candidate will be adept at interacting, communicating and partnering with other teams within AWS such as product teams, solutions architecture, sales, marketing, business development, and professional services, as well as representing your team to executive management. You will also look for patterns and trends that can be broadly applied across an industry segment or a set of customers that can help accelerate innovation.

　　This is an opportunity to be at the forefront of technological transformations, as a key technical leader.

　　We are open to hiring candidates to work out of one of the following locations:

　　East Palo Alto, CA, USA | San Francisco, CA, USA | Santa Clara, CA, USA

　　Basic Qualifications

　　3+ years of building machine learning models for business application experience

　　PhD, or Master's degree and 6+ years of applied research experience

　　Experience programming in Java, C++, Python or related language

　　Experience with neural deep learning methods and machine learning

　　8+ years of specific technology domain areas (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics) experience

　　3+ years of design, implementation, or consulting in applications and infrastructures experience

　　10+ years of IT development or implementation/consulting in the software or Internet industries experience

　　Experience with GenAI models across training, inference, MLOPs, and/or developing GenAI applications.

　　5+ years of experience training large models across compute types (e.g., GPUs, custom instances), and developing applications powered by GenAI models.

　　Deep hands-on understanding of deep learning and other ML algorithms and infrastructure.

　　Strong verbal and written communications skills and ability to lead effectively across organizations.

　　Preferred Qualifications

　　Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.

　　Experience with large scale distributed systems such as Hadoop, Spark etc.

　　5+ years of infrastructure architecture, database architecture and networking experience

　　Experience working with end user or developer communities

　　10+ years of pre-sales engineering, technical business development, enterprise sales, strategic partnerships, or program/product management experience.

　　Thorough understanding of AI/ML technology stack.

　　Experience with cloud computing and or related fields.

　　Experience with one or more general purpose programming languages, including but not limited to: C/C++, C#, Python.

　　Good communication skills, business, and financial acumen.

　　Demonstrated ability to work effectively across internal and external organizations is key.

　　Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.

　　Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

　　Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $260,000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. Applicants should apply via our internal or external career site.