Our client is currently seeking a Sr. Machine Learning Engineer

Machine Learning Model Development & Optimization

• Design, develop, and optimize a wide range of machine learning models, including deep learning architectures, LLMs, and transformer‑based models (e.g., BERT classifiers).
• Build and manage distributed training workflows using PyTorch and related frameworks.
• Fine‑tune large language models and enhance inference performance using tools such as the Neuron Compiler, ONNX, and vLLM.
• Optimize models for diverse hardware platforms including GPUs, TPUs, AWS Inferentia, and Trainium.

AI Infrastructure & Services Architecture

• Design end‑to‑end AI service architectures supporting real‑time streaming and offline batch processing.
• Lead the development of ML infrastructure encompassing data ingestion, feature engineering, model training, deployment, and monitoring.
• Build scalable inference systems for both real‑time and batch prediction workloads.
• Deploy machine learning models across multiple compute environments such as EC2, EKS, SageMaker, and specialized inference hardware.

MLOps Platform Engineering & Pipeline Automation

• Develop and maintain MLOps platforms, including Feature Stores, ML Observability, Governance frameworks, and automated training/deployment pipelines.
• Create end‑to‑end automated workflows for training, validation, and production deployment using infrastructure‑as‑code.
• Build internal tooling to simplify and abstract engineering complexities for data science teams.
• Implement CI/CD pipelines for model artifacts, infrastructure components, and deployment workflows.

Performance Optimization & Cross‑Functional Collaboration

• Monitor and optimize ML systems for accuracy, throughput, latency, reliability, and cost efficiency.
• Conduct system profiling and implement observability solutions across the entire ML lifecycle.
• Partner with data engineering teams to ensure efficient data delivery formats, quality, and ingestion cadence.
• Work closely with data architecture, governance, and security teams to ensure compliance with organizational standards.
• Provide technical mentorship and guidance on modeling strategies, infrastructure design, and best practices.

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