Senior MLOps Engineer

We run a Large Behavioral Model — custom transformer-based architecture — that generates billions of spatiotemporal predictions daily for Fortune 500 clients. The model is retrained on a regular cadence and serves predictions at scale.

You'll own the infrastructure that makes that possible. ML pipelines, cloud infrastructure across AWS and GCP, CI/CD for models, cost management, and monitoring. You'll work with ML engineers who depend on your systems being reliable, reproducible, and fast.

• Build and maintain training and inference pipelines for the LBM, handling millions of predictions daily
• Operate scalable cloud infrastructure on AWS and GCP for training, deployment, and monitoring
• Optimize cloud resource usage — cost-effective scaling without sacrificing availability or performance
• Maintain CI/CD pipelines specifically for ML models, with proper dev / staging / prod separation
• Implement automation for model lifecycle management, retraining, and data pipeline orchestration
• Instrument model monitoring — performance, drift, latency, resource utilization — and wire up alerting
• Collaborate with ML engineers and product teams on model performance, retraining cadence, and infrastructure
• Document pipelines, deployments, and on-call runbooks
• Evaluate new MLOps tooling and techniques, and apply them where they measurably improve the platform

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• Bachelor's, Master's, or PhD in CS, Engineering, AI, or a related field
• 3-5 years of hands-on experience in MLOps, ML platform, or ML infrastructure
• Production experience deploying, managing, and scaling ML workloads on AWS or GCP (SageMaker, Vertex AI, EC2, GKE, EKS)
• Strong proficiency with Docker, Kubernetes, and container orchestration
• Experience designing and operating CI/CD pipelines for ML models
• Performance tuning and cost optimization experience in cloud environments
• Strong programming skills in Python
• Working experience with PyTorch (or TensorFlow) in production
• Solid understanding of machine learning concepts, including deep learning and transformer-based architectures
• Strong written English and comfort working with a US-based team across time zones

• Production experience with both AWS and GCP

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• Experience serving transformer-based models at scale (TorchServe, Triton, vLLM, or similar)
• Distributed training experience (DDP, FSDP, Ray, or similar)
• Terraform or other infrastructure-as-code
• Feature store experience (Feast, Vertex AI Feature Store, SageMaker Feature Store, or similar)
• Prometheus, Grafana, or similar observability stacks
• Prior experience in a fast-paced startup environment
• AdTech, MarTech, or consumer behavioral data domains

• Full-time, fully remote within India
• Salary: ₹25-40 LPA depending on experience
• Overlap with US hours: 3-4 hours with US Eastern, typically evenings IST
• Hands-on work with a modern ML stack at petabyte scale
• Direct collaboration with ML engineers and technical leads
• Clear growth path with increasing ownership over time