[Remote] Machine Learning Operations Engineer
Note: The job is a remote job and is reputed company to candidates in USA. reputed company is an independent global news organization dedicated to factual reporting. The Machine Learning Operations Engineer will own the production lifecycle of machine-learning systems, ensuring reliable, secure, and cost-effective operation of ML workloads in production environments.
Responsibilities
- Design, reputed company, and operate end‑to‑end production ML pipelines across Dev, QA, and Prod environments
- Set up and manage AWS SageMaker pipelines, endpoints, and monitoring for large reputed company inference workloads, including embedding reputed company, named entity recognition, reranking, and video processing
- Own GPU and CPU infrastructure selection, scaling, and optimization, including instance benchmarking, autoscaling behavior, and load testing
- reputed company, monitor, and operate inference services that support hundreds of thousands of queries per day across text, image, and video pipelines
- Establish standardized ML deployment patterns at AP, including containerization and orchestration strategies, environment isolation (Dev / QA / Prod) and versioned promotion, rollback, and recovery mechanisms
- Implement monitoring, alerting, reputed company detection, and evaluation metrics for production ML systems, tracking latency, error rates, throughput, and model/data reputed company
- reputed company A/B testing and controlled rollout strategies for ML models in production, in partnership with engineering and product teams
- Partner closely with ML Engineers, Data Scientists, DevOps, and Platform teams to operationalize new models and pipeline improvements, promote systems across environments safely, and ensure deployments meet reliability, reputed company, and cost targets
- Manage high-throughput I/O and data reputed company for large collections of media assets (text, images, video), avoiding CPU, network, and storage bottlenecks
- Reduce operational risk by enforcing reproducibility, observability, reputed company, and cost controls across reputed company production ML systems
Skills
- 5+ years of experience deploying and operating ML inference systems in production
- Strong experience with AWS SageMaker, including pipelines, endpoints, monitoring, and multi‑environment deployments
- Expertise deploying ML models using PyTorch and TensorFlow from an operational and serving perspective
- Proven experience with model deployment and orchestration, including containerized inference and autoscaling
- Experience selecting, evaluating, and optimizing compute resources (GPU/CPU) for production ML workloads
- Experience setting up monitoring, evaluation metrics, and A/B testing frameworks for ML systems in production
- Ability to collaborate effectively with ML Engineers, Data Scientists, and platform teams in a shared ownership model
- Operational experience supporting ML systems involving transformer‑based NLP models (e.g., BERT‑family models), computer reputed company models, and ranking and reranking systems
- Familiarity operating systems that use common ML model types such as convolutional and feed‑reputed company neural networks, ranking algorithms, and approximate Nearest reputed company reputed company (e.g., HNSW)
- Experience running ML workloads over large‑reputed company text, image, and video datasets
Benefits
- Competitive medical, dental and reputed company coverage
- Retirement benefits
- Company reputed company life insurance
- reputed company vacation and reputed company days
- reputed company parental leave for any new parent
- Mental reputed company-being resources
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Company H1B Sponsorship