MLOps Engineer - NYC, NY - Hybrid

Position: MLOps Engineer

Location: NYC, NY

Mode Of Hire: Contract

Mode Of Work: Hybrid (3 days from office)

Job Description: 

• 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 and CPU infrastructure selection, scaling, and optimization, including instance benchmarking, autoscaling behavior, and load testing.
• Deploy, 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)
  • 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 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
  • 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, and cost controls across all production ML systems.

Required Skills & Experience

• Hands‑on 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.

Strongly Preferred

• Operational experience supporting ML systems involving:
  • Transformer‑based NLP models (e.g., BERT‑family models)
  • Computer vision models
  • Ranking and reranking systems
• Familiarity operating systems that use common ML model types such as:
  • Convolutional and feed‑forward neural networks
  • Ranking algorithms
  • Approximate Nearest Neighbor methods (e.g., HNSW)
• Experience running ML workloads over large‑scale text, image, and video datasets.