Offer summary

Qualifications:

Bachelor's or Master's degree in Computer Science or related field., Minimum 4+ years of industry experience in MLOps, especially on GCP., Proven expertise in building and scaling ML inference platforms and agentic AI systems., Hands-on experience with deep learning frameworks like TensorFlow, PyTorch, and HuggingFace..

Key responsibilities:

Architect and optimize ML inference platforms supporting advanced models.

Collaborate with teams to develop scalable engineering solutions from business needs.

Lead the development and operation of high-performance, cost-effective inference systems.

Develop and automate CI/CD workflows for ML models and data pipelines.

Job description

About the Role: 100% REMOTE!!!

We are looking for a seasoned Machine Learning Operations (MLOPs) Engineer to build, and optimize ML inference platform. The role demands an individual with significant expertise in Machine Learning engineering and infrastructure, with an emphasis on building Machine Learning inference systems. Proven experience in building and scaling ML inference platforms in a production environment is crucial. This remote position calls for exceptional communication skills and a knack for independently tackling complex challenges with innovative solutions.

Work Location: 100% Remote

Key Responsibilities

Architect and optimize ML Platforms to support cutting-edge machine learning and deep learning models.

Collaborate closely with cross-functional teams to translate business objectives into scalable engineering solutions.

Lead the end-to-end development and operation of high-performance, cost-effective inference systems for a diverse range of models, including state-of-the-art large language models (LLMs).

Provide technical leadership and mentorship to cultivate a high-performing engineering team.

Develop CI/CD workflows for ML models and data pipelines using tools like Cloud Build, GitHub Actions, or Jenkins.

Automate model training, validation, and deployment across development, staging, and production environments.

Monitor and maintain ML models in production using Vertex AI Model Monitoring, logging (Cloud Logging), and performance metrics.

Ensure reproducibility and traceability of experiments using ML metadata tracking tools like Vertex AI Experiments or MLflow.

Manage model versioning and rollbacks using Vertex AI Model Registry or custom model management solutions.

Collaborate with data scientists and software engineers to translate model requirements into robust and scalable ML systems.

Optimize model inference infrastructure for latency, throughput, and cost efficiency using GCP services such as Cloud Run, Kubernetes Engine (GKE), or custom serving frameworks.

Implement data and model governance policies, including auditability, security, and access control using IAM and Cloud DLP.

Stay current with evolving GCP MLOps practices, tools, and frameworks to continuously improve system reliability and automation.

Qualifications

Technical degree: Bachelor's degree in Computer Science with a minimum of 6+ years of relevant industry experience, or

A Master's degree in Computer Science with at least 4+ years of relevant industry experience. Proven experience in implementing MLOps solutions on Google Cloud Platform (GCP) using services such as Vertex AI, Cloud Storage, BigQuery, Cloud Functions, and Dataflow.

Proven experience in building and scaling agentic AI systems in production environments.

Hands-on experience with leading deep learning frameworks such as TensorFlow, Pytorch, HuggingFace, Langchain, etc.

Solid foundation in machine learning algorithms, natural language processing, and statistical modeling.

Strong grasp of fundamental computer science concepts including algorithms, distributed systems, data structures, and database management.

Ability to tackle complex challenges and devise effective solutions. Use critical thinking to approach problems from various angles and propose innovative solutions.

Worked effectively in a remote setting, maintaining strong written and verbal communication skills. Collaborate with team members and stakeholders, ensuring clear understanding of technical requirements and project goals.

Travel

Travel as per business requirements

Sponsorship

Candidate must be legally able to work for any employer in the US

This role is not sponsorship eligible

Required profile