Overview

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
  • Upper-Intermediate English proficiency (B2, Upper-Intermediate)

Nice to have

  • Experience with Amazon Lookout for anomaly detection on industrial or operational data

[GTS] Benefits (generic, except India)

  • International projects with top brands
  • Work with global teams of highly skilled, diverse peers
  • Healthcare benefits
  • 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