Join a high-growth, cloud-native SaaS company transforming the travel and hospitality space through advanced machine learning. As a Senior Machine Learning Platform Engineer, you will design, build, and operate ML infrastructure supporting the full lifecycle of training, deployment, inference, monitoring, and retraining of thousands of customer-specific models in a multi-tenant environment.
Key Highlights
Technical Skills Required
Benefits & Perks
Job Description
Job Title: Senior ML Platform Engineer, AWS & MLOps
Compensation: $190,000 Base + Bonus + Equity
Location: Remote (US-based)
About the Company
A high-growth, cloud-native SaaS company transforming the travel and hospitality space through advanced machine learning. Their platform is used by thousands of customers globally, with ML at the heart of real-time pricing and forecasting capabilities.
The Mission
This organization is investing deeply in data infrastructure and ML to power smarter decision-making at scale. With a fully remote team and strong financial backing, they're building the future of travel tech through automation, scalability, and intelligent systems.
The Role
We're looking for a senior-level ML Platform Engineer to own and evolve the infrastructure that supports large-scale, production-grade ML workflows. This role is not focused on modeling or data science; instead, it's about building reliable, scalable systems that enable training, deployment, monitoring, and retraining of thousands of customer-specific models in a multi-tenant environment.
What You’ll Do
- Design, build, and operate ML infrastructure supporting the full lifecycle: training, deployment, inference, monitoring, and retraining
- Architect systems that scale to thousands of models running in parallel
- Own ML pipelines end to end, including CI/CD, versioning, and rollback strategies
- Optimize batch and real-time inference workloads for cost and reliability
- Implement observability frameworks for ML systems, tracking model health, drift, and performance
- Collaborate with data scientists to streamline productionization of models
- Evaluate and integrate AWS-native and open-source MLOps tools
What You’ll Bring
- ML platform engineering, MLOps, or related infrastructure-heavy ML roles
- Strong AWS expertise, particularly with SageMaker, Lambda, ECR, and S3
- Production experience with Kubernetes (EKS) for training and inference
- Proven ownership of end-to-end ML pipelines in production
- Deep understanding of ML system monitoring and reliability
- Strong Python skills and solid software engineering fundamentals
- Collaborative mindset with experience partnering with data science teams
Tech Stack
- AWS: SageMaker, Lambda, ECR, EMR, S3, CloudWatch
- Kubernetes (EKS), Docker
- Terraform, GitHub Actions (or similar CI/CD tools)
- MLflow, model registries, feature stores
- Prometheus, Grafana
- Python
Why Join?
- Join a mission-driven company where ML drives real-world impact at scale
- Build infrastructure that supports real-time, high-value decision-making
- Fully remote, collaborative team with strong engineering culture
- Opportunity to shape the future of MLOps in a high-growth domain
About People In AI
People In AI is a specialized talent partner to the world’s most exciting AI companies. We help fast-growing teams find, attract, and hire exceptional talent across machine learning, data, and engineering.
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