Amazon Web Services is seeking a Machine Learning Solutions Architect to support our Intelligence Community customers in the Maryland, Northern Virginia and Washington, DC area. This position requires that the candidate selected be a U.S. Citizen and must currently possess an active Top Secret security clearance. The position further requires that, after start, the selected candidate obtain and maintain an active TS/SCI security clearance with polygraph and satisfy other security related requirements.
Are you excited to help the Intelligence Community (IC) to leverage massive amounts of data in developing Machine Learning (ML) and Deep Learning (DL) models and adopting Artificial Intelligence (AI)? Eager to learn from many different enterprise use cases of AWS ML and Deep Learning (DL)? 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? Come join us!
At Amazon Web Services (AWS), we are helping the federal government build ML and DL models on the AWS Cloud. We are applying predictive technology to large volumes of data and against a wide spectrum of problems.
ML and AI are rapidly growing in importance. We're seeing more and more amazing AI work being done from autonomous vehicles and imagery analysis to financial trading and shipping logistics. Given the scale required for developing AI models, the cloud is an ideal place to deploy AI models, and Amazon Web Services (AWS) is the leader in the deployment of AI. We're looking for someone passionate and deeply excited about this space. Someone who is devoted to helping IC customers understand how AI can make a big difference to their mission.
As a Machine Learning Specialist Solutions Architect (SA), you will be the Subject Matter Expert (SME) for designing machine learning solutions that leverage AWS services to automate solutions and drive down costs for customers. As part of the Specialist Solutions Architecture team, you will work closely with the other Specialist SAs to enable large-scale customer use cases and drive the adoption of AWS for ML/AI platforms. You will interact with other SAs in the field, providing guidance on their customer engagements, and you will develop white papers, blogs, reference implementations, and presentations to enable customers and partners to fully leverage ML/AI on AWS. You will also create field enablement materials for the broader SA population, to help them understand how to integrate AWS ML solutions into customer architectures.
You must have deep technical experience working with technologies related to artificial intelligence, machine learning and/or deep learning. A strong statistics, mathematics, computer science, or technology background is preferred, in addition to experience helping customers architect enterprise grade AI/ML solutions. You will be familiar with the ecosystem of software vendors in the AI/ML space, in areas such as Natural Language Processing (NLP), classification modelling, unsupervised learning, and image recognition as well as be able to operate in Python data science environments. You must have the ability to leverage this knowledge to help AWS customers in their software selection process.
Candidates must have great communication skills and be very technical, with the ability to impress AWS customers at any level, from executive to developer. Previous experience with AWS is desired but not required, provided you have experience building large scale solutions. You will get the opportunity to work directly with senior engineers at customers, partners and AWS service teams, influencing their roadmaps and driving innovation.
If you are someone who enjoys innovating, likes solving hard problems and working on the cutting edge of technology, we would love to have you on the team.
Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and we host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon's culture of inclusion is reinforced within our 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.
For more information please reach out to Dane Perera (dperera@)
Bachelor's degree in computer science, engineering, mathematics, or related field of study OR 5+ years of professional or military work experience
5+ years within specific technology domain areas (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics)
2+ years of design, implementation, or consulting experience in applications and infrastructures
Experience communicating across technical and non-technical audiences, including executive level stakeholders or clients
2+ years experience in the field of AI, Machine Learning, Deep Learning, NLP and related technologies.
2+ years experience developing AI models in real-world environments and integrating AI/ML and other AWS services into large-scale production applications.
2+ years design/implementation/consulting experience architecting and building cloud solutions.
2+ years professional experience in software development in languages like Python, R, Scala, or Java. Experience working with RESTful API and general service oriented architectures.
Experience influencing and building mindshare convincingly with any audience. Confident and experienced in public speaking to large audiences.
Current, active US Government Security Clearance of Top Secret or above
Professional experience architecting/operating solutions built on AWS
Experience communicating effectively across internal and external organizations, for complex mission-critical solutions
Experience with predictive analytics, semi- and unstructured data
Experience deploying production-grade machine learning solutions on public cloud platforms
Data science background and experience manipulating/transforming data, model selection, model training, cross-validation and deployment at scale.
Experience with Machine and Deep Learning toolkits such as MXNet, TensorFlow, Caffe and Torch.
Experience with AWS services related to AI/ML highly desirable, particularly Amazon SageMaker, Amazon EMR, AWS Lambda, Machine Learning, IoT, Amazon DynamoDB, Amazon S3, Amazon EC2 Container Service, Greengrass etc.