AI/ML Deployment Engineer

Role: AI/ML Deployment Engineer

Location: Austin TX OR Seattle WA On-site

Duration : 12 months

we need a senior candidate here at least 15+ years, but should be hands on (No Architect or Manager)

OVERVIEW:

We are seeking a highly skilled and specialized AI/ML Model Deployment Specialist / Production MLOps Engineer. The core focus of this role is to take existing machine learning models and optimize their deployment and serving infrastructure for high-performance, production-ready inference, with a strong emphasis on leveraging state-of-the-art AI model serving technologies.

REQUIRED SKILLSETS:

AI/ML Domain Expertise

• Deep understanding of the AI/ML domain, with the core effort centered around model performance and serving, rather than general infrastructure.

ML Frameworks

• Expertise in PyTorch and TensorFlow: Proven ability to work with and troubleshoot model-specific dependencies, logic, and graph structures within these major frameworks.

Inference Optimization

• Production Inference Experience: Expertise in designing and implementing high-throughput, low-latency model serving solutions.
• Specialized Inference Servers: Mandatory experience with high-performance inference servers, specifically including vLLM, or similar dedicated LLM serving frameworks.
• GPU Optimization: Demonstrated ability to optimize model serving parameters and infrastructure to maximize performance on NVIDIA or equivalent GPU hardware.

Deployment and Infrastructure

• Containerization (Docker): Proficiency in creating minimal, secure, and efficient Docker images for model and server deployment.
• Infrastructure Knowledge (Helpful, but Secondary): General knowledge of cloud platforms (AWS, Google Cloud Platform, Azure) and Kubernetes/orchestration is beneficial but the primary focus remains on model serving and optimization.