Job Title: Cloud AI/ML Engineer - Generative AI (AWS)
About the Role
We are seeking a skilled and forward-thinking
Cloud AI/ML Engineer
to lead the design, development, and support of scalable, secure, and high-performance
generative AI applications on AWS
. You'll operate at the crossroads of cloud engineering and artificial intelligence, enabling rapid and reliable delivery of cutting-edge AI solutions using services like
Amazon Bedrock
and
SageMaker
. This is an opportunity to join a collaborative team driving innovation in AI infrastructure, with a strong focus on automation, security, observability, and performance optimization.
Roles and Responsibilities
1. AI/ML Integration
Utilize
Amazon Bedrock
for leveraging foundation models and
Amazon SageMaker
for training and deploying custom models.
Design and maintain scalable generative AI applications using AWS-native AI/ML tools and services.
2. Deployment & Operations
Build and manage
CI/CD pipelines
to automate infrastructure provisioning and model lifecycle workflows.
Monitor infrastructure and model performance using
Amazon CloudWatch
and other observability tools.
Ensure production-grade availability, fault tolerance, and performance of deployed AI systems.
3. Security & Compliance
Enforce security best practices using
IAM
, data encryption, and access control policies.
Maintain compliance with relevant organizational, legal, and industry-specific data protection standards.
4. Collaboration & Support
Partner with
data scientists, ML engineers, and product teams
to translate requirements into resilient cloud-native solutions.
Diagnose and resolve issues related to model behavior, infrastructure health, and AWS service usage.
5. Optimization & Documentation
Continuously assess and optimize
model performance
,
infrastructure cost
, and
resource utilization
.
Document deployment workflows, architectural decisions, and operational runbooks for team-wide reference.
6. Mentorship & Guidance
Mentor peers and junior engineers by sharing knowledge of AWS services and
generative AI best practices
.
Must-Have Skills & Experience
Expertise in
AWS services
, particularly
SageMaker, Bedrock, EC2, IAM
, and related cloud-native tools.
Strong coding skills in
Python
, with experience in developing AI applications.
Hands-on experience with
Docker
for containerization and familiarity with
Kubernetes
for orchestration.
Proven experience building and maintaining
CI/CD pipelines
for AI/ML workloads.
Knowledge of
data security
, access control, and monitoring within cloud environments.
Experience managing
cloud-based data flows
and infrastructure for ML workflows.
Good-to-Have (Preferred) Skills
AWS certifications, such as:
+
AWS Certified Machine Learning - Specialty
+
AWS Certified DevOps Engineer
Understanding of
responsible AI practices
, particularly in generative model deployment.
Experience in
cost optimization
,
auto-scaling
, and
resource management
for production AI workloads.
Familiarity with tools like
Terraform, CloudFormation
, or
Pulumi
for infrastructure as code (IaC).
Exposure to
multi-cloud
or hybrid cloud strategies involving AI/ML services.
Skills
Aws,Python,Docker,Kubernets
About UST
UST is a global digital transformation solutions provider. For more than 20 years, UST has worked side by side with the world's best companies to make a real impact through transformation. Powered by technology, inspired by people and led by purpose, UST partners with their clients from design to operation. With deep domain expertise and a future-proof philosophy, UST embeds innovation and agility into their clients' organizations. With over 30,000 employees in 30 countries, UST builds for boundless impact--touching billions of lives in the process.
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