Senior Machine Learning Engineer

We are seeking a highly skilled and experienced Senior Machine Learning Engineer to drive the development, deployment, and monitoring of cutting-edge machine learning solutions in a scalable cloud environment. You will collaborate with a cross-functional team to deliver end-to-end ML pipelines, ensure high-quality model performance, and implement robust monitoring for production systems.

Responsibilities

Reproduce training environments with pinned dependencies and seeds, rebuild feature and label pipelines, and register models Deploy Sage. Maker endpoints and batch jobs with autoscaling, execute shadow/canary strategies, and define rollback protocols Set up ML experiments, Model Registry, and Feature Stores, integrating them into CI/CD workflows Implement Model Monitor for drift and performance tracking, configure alerting mechanisms, and create comprehensive runbooks Optimize pipelines for reliable model serving, ensuring low latency, high quality, and rollback readiness during hypercare Collaborate with data engineers to build scalable data pipelines on S3/Snowflake for training and inference workflows Requirements 3+ years of hands-on experience in machine learning engineering, with a solid background in Python 3.

x Proficiency in Py. Torch or Tensor. Flow, with a track record of building and deploying models in production Expertise in AWS Sage. Maker, including Pipelines, Registry, Endpoints, and Model Monitor Familiarity with MLflow or Sage. Maker Experiments for experiment tracking and artifact management Strong skills in Docker for containerization of ML workloads Experience with Feature Store solutions such as Sage.

Maker Feature Store or Feast English level B1+ for effective communication Nice to have Proficiency in FastAPI or BentoML for lightweight API service development Knowledge of ONNX for model optimization and cross-platform interoperability Familiarity with distributed inference tools like Ray Serve or Triton Experience with large-scale distributed training using Horovod or Deep.

Speed Understanding of Hugging Face Transformers for state-of-the-art NLP model implementation