Description

WWPS is looking for a talented Machine Learning Consultant who will collaborate with other scientists and engineers to develop AI capabilities to address customer use-cases at enterprise scale.

This individual will play a pivotal role in architecting, executing, and deploying scalable artificial intelligence and machine learning solutions to navigate our customer's most complex challenges.

Join us and implement best practices in software development and DevOps to machine learning deployment, leveraging AWS's robust ecosystem.

In this position, you will help guide teams in architecting and implementing innovative, AWS Cloud-native ML solutions, providing direct and immediate impact for your customers.

You will work closely with talented data scientists and engineers to put algorithms and models into practice to help solve our customers' most challenging problems.

You will also guide teams in the development of new solutions and aid customers in adopting AWS ML capabilities. Come join us to help make AIML adoption and impact a reality for our customers.

This position requires that the candidate selected must currently possess and maintain an active TS/SCI security clearance. The position further requires that, after start, the selected candidate obtain and maintain an active TS/SCI security clearance with polygraph or commensurate clearance for each government agency for which they perform AWS work.

If you have questions or would like to submit a referral, please reach out to Anh Tran at anhttran@amazon.com.

Key job responsibilities

Engage directly with customers to understand the business problems and aid them in implementing their ML solutions.

Deliver Machine Learning projects from beginning to end. This includes understanding the business need, planning the project, aggregating & exploring data, building & validating predictive models, and deploying completed ML capabilities on the AWS Cloud to deliver business impact for the customer.

Spearhead the the fine-tuning of machine learning pipelines for optimal performance and efficiency, utilizing AWS technologies. Implement automation tools and processes for model deployment, monitoring, and scaling.

Assist customers with Machine Learning Operations (MLOps) workflows such as model deployment, retraining, testing, and performance monitoring, leveraging AWS's robust ecosystem, including SageMaker, Container Services, ECS and EKS, and EC2.

Experience applying best practices from core Software Development activities to Machine Learning (deployability, unit testing, well structured extensible software, etc.)

Collaborate with data scientists and software engineers to deploy machine learning models ensuring resource utilization, and cost tracking and savings. Architect and implement solutions to scale machine learning inference to handle large workloads efficiently

Aid in the design and develop sophisticated machine learning algorithms and models, enhancing the efficiency, scalability, and reliability of AWS services.

Experiment with or implement custom foundational models for our customer's emerging GenerativeAI needs.

Remain at the forefront of machine learning and cloud computing advancements, integrating novel techniques and methodologies to foster innovation at AWS and with our customers

Provide mentorship and technical leadership to less experienced team members, nurturing an environment of learning and continuous enhancement.

Actively participate in the broader machine learning community by attending conferences, workshops, and contributing to open-source initiatives.

A day in the life

Our team tackles a diverse array of customer needs at the intersection of various AIML domains and our customers’ national security missions, leveraging a range of AWS Services (SageMaker, Translate, Transcribe, Rekognition, etc.), open source, and custom capabilities to deliver on the toughest challenges in national security.

You’ll collaborate closely with a diverse team and spend time discussing how to integrate machine learning models into customer mission sets, and ensuring those solutions can scale, be reliable, and be optimized in the future.

Later in the day, you may focus on deploying a model using AWS Sagemaker in the customer’s unique environment, monitoring its performance, and tweaking parameters for optimization.

Throughout the day you’ll engage in MLOps practices, streamlining the machine learning lifecycle from development to deployment.

As you wrap up your day, you’ll document your findings, share insights with the team, and maybe mentor a junior colleague or prepare a presentation on your latest projects.

Every day brings new challenges and the chance to innovate at the bleeding edge of cloud computing, AI, and the national security mission.

About the team

Work/Life Balance

Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.

Mentor-ship & Career Growth

Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentor ship. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded Evaluator and enable them to take on more complex tasks in the future.

Inclusive Team Culture:

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 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.

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

Arlington, VA, USA | Herndon, VA, USA

Basic Qualifications

• Bachelor's degree in computer science or equivalent.

• 5+ years of non-internship professional software development experience.

• 2+ years of experience building CI/CD pipelines.

• 2+ years of architecting solutions to productize ML models, MLOps, and feedback and training systems.

• Current, active US Government Security Clearance of Top Secret or above.

Preferred Qualifications

• Experience programming with at least one modern language such as C++, C#, Java, Python, Golang, PowerShell, Ruby.

• Experience working in an Agile environment using the Scrum methodology.

• Experience with CI/CD pipelines build processes.

• Experience in automating, deploying, and supporting large-scale infrastructure.

• Experience building services using AWS products.

• Experience utilizing AWS cloud solutions in a DevOps environment.

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.