Associate Tech Specialist

Location : San Jose, CA

Job Description : AI / ML Model Deployment Specialist / Production MLOps Engineer

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.

Responsibilities

Model-Specific Optimization :

Analyze and understand the underlying logic and dependencies of various AI / ML models (primarily using PyTorch and TensorFlow) to identify bottlenecks in the inference pipeline.

High-Performance Serving Implementation :

Design, implement, and manage high-performance inference serving solutions utilizing specialized inference servers (e.g., vLLM) to achieve low latency and high throughput.

GPU Utilization Optimization :

Optimize model serving configurations specifically for GPU hardware to maximize resource efficiency and performance metrics in a production environment.

Containerization for Deployment :

Create minimal, secure, and production-ready Docker images for streamlined deployment of optimized models and inference servers across various environments.

Collaboration :

Work closely with core engineering and data science teams to ensure a smooth transition from model development to high-scale production deployment.

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, GCP, Azure) and Kubernetes / orchestration is beneficial but the primary focus remains on model serving and optimization.

Qualifications

Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

8+ years of experience in MLOps, Machine Learning Engineering, or a specialized Inference Optimization role.

A portfolio or project experience demonstrating successful deployment of high-performance, containerized ML models to production scale.