Engr III Cslt-AI/ML Engrg

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Skills you'll use

What you'll do

• Model Productionization & Scaling: Architect, implement, and maintain scalable infrastructure to train, fine-tune, and deploy state-of-the-art AI models (e.g., Large Language Models, Multimodal Models, Deep Learning architectures).
• Research & Development Collaboration: Work alongside Research Scientists to translate novel theoretical concepts and prototypes into clean, modular, and performant codebases.
• Inference & Training Optimization: Optimize model inference and training workflows for memory, speed, and cost efficiency using techniques like quantization, pruning, distillation, and distributed execution.
• ML Pipelines & Data Architecture: Design resilient data pipelines, dataset curation workflows, and automated evaluation suites to ensure continuous model improvement and regression testing.
• System Design & MLOps: Build robust monitoring, logging, and continuous deployment (CI/CD for ML) systems to track model performance, drift, and system health in production.
• Technical Leadership: Code-review team contributions, mentor junior engineers, and drive technical best practices for engineering rigor within the AI Science organization.

Who can apply

• You'll need to have:
• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related quantitative field (or equivalent industry experience).
• Five or more years of professional software engineering experience with three or more years focused on building, deploying, and maintaining ML/AI models in production.
• Expert-level programming proficiency in Python and deep understanding of modern ML frameworks (PyTorch, TensorFlow, or JAX).
• Strong background in system design, object-oriented programming, data structures, and software engineering best practices.
• Hands-on experience with distributed model training and inference systems (e.g., Ray, DeepSpeed, vLLM, Megatron-LM).
• Proficiency with cloud environments (AWS, GCP, or Azure), containerization (Docker, Kubernetes), and modern MLOps toolchains.

About Verizon

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