DESCRIPTION
At Amazon Web Services (AWS), we’re hiring highly technical Data and Machine Learning engineers to collaborate with our customers and partners on key engagements. Our consultants will develop and deliver proof-of-concept projects, technical workshops, and support implementation projects. These professional services engagements will focus on customer solutions such as Machine Learning, Data and Analytics, HPC and more.
In this role, you will work with our partners, customers and focus on our AWS offerings such Amazon Kinesis, AWS Glue, Amazon Redshift, Amazon EMR, Amazon Athena, Amazon SageMaker and more. You will help our customers and partners to remove the constraints that prevent them from leveraging their data to develop business insights.
AWS Professional Services engage in a wide variety of projects for customers and partners, providing collective experience from across the AWS customer base and are obsessed about customer success. Our team collaborates across the entire AWS organization to bring access to product and service teams, to get the right solution delivered and drive feature innovation based upon customer needs.
You will also have the opportunity to create white papers, writing blogs, build demos and other reusable collateral that can be used by our customers. Most importantly, you will work closely with our Solution Architects, Data Scientists and Service Engineering teams.
The ideal candidate will have extensive experience with design, development and operations that leverages deep knowledge in the use of services like Amazon Kinesis, Apache Kafka, Apache Spark, Amazon Sagemaker, Amazon EMR, NoSQL technologies and other 3rd parties.
This is a customer facing role. You will be required to travel to client locations and deliver professional services when needed.
BASIC QUALIFICATIONS
● Bachelor’s degree in Computer Science, Engineering, Mathematics or a related field or equivalent professional or military experience
● 8+ years of experience of Data platform implementation
● 3+ years of hands-on experience in implementation and performance tuning of Kinesis, Kafka, Spark or similar implementations
● Hands on experience with building data or machine learning pipeline
● Experience with one or more relevant tools (Flink, Spark, Sqoop, Flume, Kafka, Amazon Kinesis)
● Experience developing software code in one or more programming languages (Java, JavaScript, Python, etc)
● Current experience with hands-on implementation
PREFERRED QUALIFICATIONS
● Masters or PhD in Computer Science, Physics, Engineering or Math.
● Familiar with Machine learning concepts
● Hands on experience working on large-scale data science/data analytics projects
● Hands-on experience with technologies such as AWS, Hadoop, Spark, Spark SQL, MLib or Storm/Samza.
● Experience Implementing AWS services in a variety of distributed computing, enterprise environments.
● Experience with at least one of the modern distributed Machine Learning and Deep Learning frameworks such as TensorFlow, PyTorch, MxNet Caffe, and Keras.
● Experience building large-scale machine-learning infrastructure that have been successfully delivered to customers.
● Experience defining system architectures and exploring technical feasibility trade-offs.
● 3+ years experiences developing cloud software services and an understanding of design for scalability, performance and reliability.
● Ability to prototype and evaluate applications and interaction methodologies.
● Experience with AWS technology stack.
● Written and verbal technical communication skills with an ability to present complex technical information in a clear and concise manner to a variety of audiences.
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.