MLOps Engineer

Role: MLOps Engineer Location: NYC, NY Hybrid: 3 Days Onsite Duration: Long term Roles & Responsibilities • Design, deploy, 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 scale inference workloads, including embedding generation, named entity recognition, reranking, and video processing. • Own GPU & CPU infrastructure selection, scaling, & optimization, including instance benchmarking, autoscaling behavior, & load testing. • Deploy, monitor, & operate inference services that support hundreds of thousands of queries per day across text, image, & video pipelines. • Establish standardized ML deployment patterns at AP, including: • Containerization and orchestration strategies • Environment isolation (Dev / QA / Prod) • Versioned promotion, rollback, and recovery mechanisms • Implement monitoring, alerting, drift detection, and evaluation metrics for production ML systems, tracking latency, error rates, throughput, and model/data drift. • Enable A/B testing & controlled rollout strategies for ML models in partnership with engineering & 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 • Ensure deployments meet reliability, scale, and cost targets • Manage high-throughput I/O and data movement for large collections of media assets (text, images, video), avoiding CPU, network, and storage bottlenecks. • Reduce operational risk by enforcing reproducibility, observability, security, & cost control across production ML systems. • • This role owns: • Deployment, scaling, and runtime operation of ML systems • ML infrastructure configuration and orchestration • Monitoring, alerting, A/B testing infrastructure, and drift detection • Reliability, cost control, and production governance • • This role does NOT own: • Designing model architecture • Feature engineering or data science outputs • Model accuracy or inference logic (These are owned by ML Engineers and Data Science) • • Required Skills & Experience • Hands‑on experience deploying and operating ML inference systems in production. • 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. • • Strongly Preferred • Experience running ML workloads over large‑scale text, image, and video datasets. • Operational experience supporting ML systems involving Transformer‑based NLP models (e.g., BERT‑family models), Computer vision models, Ranking & reranking systems • Familiarity operating systems that use common ML model types such as Convolutional & feed‑forward neural networks, Ranking algorithms, Approximate Nearest Neighbor methods (HNSW)