Job description

  • 4+ years of experience in technical architecture, design, deployment and operations for AI platforms, standards, protocols and devices.
  • 4+ years of experience in predictive modelling and analysis, predictive software development.
  • 3+ years professional experience in software development in languages related to ML such as Python,TypeScript and/or R.
  • Experience using machine learning libraries, such as scikit-learn, Apache MxNet, PyTorch, and TensorFlow.
  • Experience in mentoring junior team members, and guiding them on machine learning and data modeling applications.
  • Bachelor’s degree or equivalent experience
  • English language proficiency is a must.
Possible locations: Warsaw, Krakow, Wroclaw, Gdansk

Are you passionate about industrialization of Machine Learning and Deep Learning? Are you passionate about helping customers build solutions leveraging the state-of-the-art AI/ML/DL tools on Amazon Web Service (AWS)? Come join us!

Amazon Web Services (AWS) is looking for a skilled and motivated Professional Services Machine Learning Engineer for our Global Competency Center in Warsaw. AWS Professional Services is a unique organization. Our customers are the most-advanced companies in the world. We build world-class, cloud native solutions to solve real business problems for our customers. We help customers to get business outcomes with AWS. Our projects are often unique, one-of-a-kind endeavors that no one ever has done before.

Within AWS, we’re focused on bringing that knowledge and capability to customers through three layers of the AI stack: 1) Frameworks and Infrastructure with tools like Apache MxNet, PyTorch and TensorFlow, 2) Machine Learning Platforms such as Amazon SageMaker for data scientists, and, 3) API-driven Services like Amazon Lex, Amazon Polly, Amazon Transcribe, Amazon Comprehend, and Amazon Rekognition to quickly add intelligence to applications with a simple API call.

You must have deep technical experience working with technologies related to industrialisation of artificial intelligence, machine learning and/or deep learning. A strong mathematics and statistics background is preferred in addition to experience building complex machine learning models.

You will focus on delivering innovative solutions to our customers by accelerating their adoption of cloud and driving the value of their investment in AWS. Utilizing your broad and deep knowledge of technology and IT operations you will create solutions by mapping common customer business problems to reusable services focused on operational effectiveness and business value.

Roles and Responsibilities
  • Understand customer business needs and guide them to a solution using AWS AI/ML Services and Frameworks.
  • Develop MLOps (Machine Learning Operations) workflows for data preparation, deployment, monitoring and retraining.
  • Improve ML models reproducibility, auditing, and performance capabilities.
  • Collaborate with the sales and service team, as well as partner and customer stakeholders.
  • Translate business problems into ML problems and create ML solutions to produce desired customer business outcomes.
  • Develop business KPIs for machine learning model evaluations.
  • Share best practices through forums such as AWS blogs, white-papers, reference architectures and public-speaking events such as AWS Summit, AWS re: Invent, etc.
  • Act as a technical liaison between customers and the AWS SageMaker services teams to provide customer driven product improvement feedback.
As an Amazonian, you will demonstrate the Amazon Leadership Principles, coaching and mentoring others on best practices, performance and career development.

This is a customer-facing role. When appropriate and safe, you will be required to visit our main office (Warsaw) and to travel to Customer locations upon request.

About Us

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 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.

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.

Mentorship & 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 mentorship. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded professional and enable them to take on more complex tasks in the future.

A day in the life

About the hiring group

Job responsibilities

  • An MS/PhD in Computer Science, Machine Learning, Operational research, Statistics or in a highly quantitative field.
  • A strong data scientist with a background and experience in software engineering with software development / script development (Python, R, TypeScript, React, C/C++, or similar).
  • Experience working with distributed systems and grid computing
  • Ability to develop experimental and analytic plans for data modelling processes, use of strong baselines, ability to accurately determine cause and effect relations
  • Experience in AWS Platforms and Services
  • AWS Associate, Speciality or Professional Certification
  • Experience with Agile execution
  • Track record of presenting at public events such as technology conferences, hackathons or blogging, writing technical articles or contributing to open source projects.

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