Data Science Engineering Manager (Aws Sagemaker & Bedrock), San Joaquín 

We are building cloud-ready AI/ML solutions on AWS for optimization and forecasting in the energy (oil & gas) sector across parallel workstreams.
As the Data Science Engineering Manager (AWS SageMaker & Bedrock), you will blend hands-on ML engineering with stakeholder leadership to deliver production-grade outcomes on Amazon SageMaker and Amazon Bedrock—apply now to help clients achieve measurable value from AI.
Responsibilities
Lead the design, development, and deployment of optimization and forecasting models on Amazon SageMaker
Build end-to-end ML workflows covering data preparation, feature engineering, training, evaluation, and inference
Architect scalable, cost-efficient, production-ready inference solutions aligned to AWS best practices
Apply Amazon Bedrock to deliver generative AI solutions for document processing, knowledge extraction, and automation
Drive technical decisions across workstreams and provide escalation support for AI/ML topics
Coordinate delivery across multiple parallel engagements while managing priorities and timelines
Collaborate with client data teams, domain experts, and AWS Professional Services to align solutions to business goals
Implement monitoring, logging, and model governance using AWS-native tooling
Apply AWS Well-Architected Framework principles with focus on security, reliability, performance, and cost optimization
Document model architectures, pipeline configurations, and operational procedures for maintainability
Deliver knowledge transfer sessions and produce handover materials to enable client self-sufficiency
Requirements
8+ years of experience in data science and machine learning solutions development
Hands-on experience with Amazon SageMaker for training, hosting, and pipelines
Solid background in building optimization and forecasting models
Proven leadership experience managing multiple parallel client engagements
Strong stakeholder management skills with executive-level communication
Deep understanding of AWS cloud-native best practices for production AI/ML solutions
Hands-on MLOps skills across data preparation, feature engineering, evaluation, and inference
Advanced energy domain experience in oil & gas or industrial AI/ML use cases
Strong problem-solving skills with ability to operate independently across workstreams
Nice to have
Experience with Amazon Lookout for anomaly detection on industrial or operational data
We offer
International projects with top brands
Work with global teams of highly skilled, diverse peers
Employee financial programs
Paid time off and sick leave
Upskilling, reskilling and certification courses
Unlimited access to the LinkedIn Learning library and 22,000+ courses
Global career opportunities
Volunteer and community involvement opportunities
EPAM Employee Groups
Award-winning culture recognized by Glassdoor, Newsweek and LinkedIn
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