About Amazon Web Services Since 2006, Amazon Web Services has been the world’s most comprehensive and broadly adopted cloud. AWS has been continually expanding its services to support virtually any workload, and it now has more than 240 fully featured services for compute, storage, databases, networking, analytics, machine learning and artificial intelligence (AI), Internet of Things (IoT), mobile, security, hybrid, media, and application development, deployment, and management from 105 Availability Zones within 33 geographic regions, with announced plans for 18 more Availability Zones and six more AWS Regions in Malaysia, Mexico, New Zealand, the Kingdom of Saudi Arabia, Thailand. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—trust AWS to power their infrastructure, become more agile, and lower costs. To learn more about AWS, visit aws.amazon.com.
Professional Services team 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 and the assets that ProServe GCR harvested from large customer projects.
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, engineers and architects 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.
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 (Bedrock, SageMaker, OpenSearch, DynamoDB, QuickSight, etc.), open source, and custom capabilities to deliver on the toughest challenges for customers.
You’ll collaborate closely with a diverse team and spend time discussing how to integrate machine learning models or Large Language 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 or Amazon Bedrock 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(GenAI).
About the team
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
Why AWS?
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Inclusive Team Culture
Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness.
Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.
AWS is committed to a diverse and inclusive workplace to deliver the best results for our customers. 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; we celebrate the diverse ways we work. For individuals with disabilities who would like to request an accommodation, please let us know and we will connect you to our accommodation team.
aws-proserv
aws-proserve
#AWSGCR
#GCRPROSERVE
We are open to hiring candidates to work out of one of the following locations:
Beijing, 11, CHN | Shanghai, CHN | Shenzhen, CHN
Professional Services team 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 and the assets that ProServe GCR harvested from large customer projects.
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, engineers and architects 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.
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 (Bedrock, SageMaker, OpenSearch, DynamoDB, QuickSight, etc.), open source, and custom capabilities to deliver on the toughest challenges for customers.
You’ll collaborate closely with a diverse team and spend time discussing how to integrate machine learning models or Large Language 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 or Amazon Bedrock 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(GenAI).
About the team
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
Why AWS?
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Inclusive Team Culture
Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness.
Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.
AWS is committed to a diverse and inclusive workplace to deliver the best results for our customers. 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; we celebrate the diverse ways we work. For individuals with disabilities who would like to request an accommodation, please let us know and we will connect you to our accommodation team.
aws-proserv
aws-proserve
#AWSGCR
#GCRPROSERVE
We are open to hiring candidates to work out of one of the following locations:
Beijing, 11, CHN | Shanghai, CHN | Shenzhen, CHN