Note: The job is a remote job and is open to candidates in USA. Insight Global is recruiting for a global hotel chain seeking a Senior Machine Learning Engineer. The role designs, builds, deploys, and supports production machine learning and AI systems, platforms, APIs, and real-time inference services across cloud and distributed environments.
Responsibilities
- Design and build end-to-end machine learning and AI systems that power customer-facing and internal business applications
- Partner closely with Data Scientists, GenAI Engineers, and Product teams to bring AI solutions from concept to production
- Architect scalable solutions across data, machine learning, cloud infrastructure, APIs, and application layers
- Build, deploy, and manage production ML services, model-serving infrastructure, and real-time inference systems
- Develop and maintain the infrastructure that supports recommendation engines, ranking systems, forecasting models, and agentic AI applications
- Create and manage APIs, integrations, feature stores, data pipelines, and other critical ML platform components
- Own solutions throughout their lifecycle, from design and implementation to monitoring, optimization, and ongoing support
- Work across AWS, GCP, and distributed systems to ensure reliability, scalability, and performance of ML-powered applications
- Troubleshoot complex production issues and optimize systems handling real-time traffic and business-critical workloads
- Drive architectural decisions and help define how machine learning capabilities integrate with broader enterprise platforms and services
Skills
- 5+ years of experience as a Machine Learning Engineer focused on ML infrastructure, ML platforms, and production machine learning systems
- Experience building and owning end-to-end ML platforms, including model training, deployment, serving, monitoring, and operational support
- Deep expertise with AWS, including services such as SageMaker, ECS, Lambda, OpenSearch, S3, Route 53, DynamoDB, VPCs, Subnets, Security Groups, and Load Balancers
- Strong understanding of cloud architecture, networking, and distributed systems within AWS environments
- Experience building and supporting real-time, production-scale applications and machine learning services
- Experience designing and building high-performance APIs, with a deep understanding of system integrations, service dependencies, scalability, and architectural tradeoffs
- Experience building systems that transform and serve data for machine learning model training and inference
- Hands-on experience with MLOps tooling, including CI/CD pipelines, model deployment, monitoring, and operational automation
- Experience with feature stores and vector databases/vector stores supporting modern AI and machine learning workloads
- Understanding of both batch and streaming data processing and when to use each approach
- Experience building machine learning solutions such as recommendation engines, search systems, ranking systems, fraud detection platforms, or similar large-scale ML products
- Ability to clearly articulate end-to-end system architecture, engineering decisions, and how models, APIs, infrastructure, and downstream systems interact in production
- Master's degree in Computer Science, Machine Learning, Data Science, Engineering, Mathematics, Statistics, or a related technical field
- GCP
- Snowflake
- Exposure to Applied Science work
- Master's in Applied Science
- PhD in one of the sciences
Benefits
- Starting on the 31st day of employment: medical, dental, and vision insurance
- Starting on the 31st day of employment: HSA, FSA, and DCFSA account options
- Starting on the 31st day of employment: 401k retirement account access with employer matching
- Paid sick leave and/or other paid time off as provided by applicable law
Company Overview
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