Machine Learning Platform Architect

3 weeks ago

Santa Ana, California, United States First American Full time
Overview

We are a leading provider of title and tax data, analytics, and title automation for title companies nationwide. Our technology enables quick access to title history information, property tax assessment and payment data, document images, and property files in major metropolitan areas across the United States.

Salary Range
$109,600 - $146,170 Annually

This hiring range is a reasonable estimate of the base pay range for this position at the time of posting. Pay is based on a number of factors which may include job-related knowledge, skills, experience, business requirements and geographic location.

Job Description

Machine Learning Engineer Role:
Responsible for building and implementing deep learning-based transformer-based machine learning models in the areas of Natural Language processing (NLP) and Computer Vision (CV). Build and manage the infrastructure on cloud to deploy Machine Learning models in production in conformance with organization's security and compliance needs. Experience in fine tuning Large Language Models (LLM's) to task specific data sets. Deploy LLM and deep learning models in production and optimize real time inference on millions of predictions daily.

R&D Role: Responsible for ML Operations with expertise in fast serving inference, ML flow, research, and development (R&D), and a strong focus on best practices in ML development and operations. Develop and maintain robust and efficient ML infrastructure, ensuring smooth ML flow from development to production, drive innovative R&D initiatives, and implement industry-leading best practices. Monitor and address model drift to ensure model performance and accuracy over time. Drive R&D initiatives to explore and implement innovative ML operations techniques, deployment technologies, and monitoring frameworks. Ensure efficient data pipelines and data availability for ML model serving and monitoring.

Engineering Role: Build and manage the infrastructure on cloud (Azure or GCP or Databricks) to deploy Machine Learning models in production in conformance with organization's security and compliance needs. Experience in Infrastructure as Code (IaC) automation and a strong background in software engineering and machine learning, as well as experience building and maintaining large-scale machine learning models in production. Implement and maintain model monitoring tools and processes to track key metrics, generate alerts, and facilitate proactive model maintenance. Work closely with cross-functional teams to identify and address infrastructure bottlenecks, optimize resource allocation, and improve scalability of ML systems.

Responsibilities:
  • Develop and optimize ML infrastructure for fast serving of machine learning models in production environments, ensuring low-latency and high-throughput inference capabilities.
  • Implement and maintain efficient ML flow processes, including model versioning, deployment, and monitoring to enable seamless transition from development to production.
  • Collaborate with cross functional teams, data scientists, and other engineering disciplines to deploy ML models, ensure code quality, reproducibility, adherence to best practices in ML development, smooth integration with existing production systems, and create efficient data pipelines and data availability for ML modeling.
  • Monitor and analyze model performance and accuracy over time, detecting and addressing model drift to maintain reliable and up-to-date ML systems.
  • Stay up to date with the latest advancements in ML operations, deployment technologies, and monitoring frameworks through continuous learning and experimentation. Research, adopt, and promote best practices to enhance the efficiency and effectiveness of ML infrastructure.
  • Document processes, best practices, and lessons learned to facilitate efficient ML operations and contribute to department/company knowledge base.

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