Machine Learning Engineer

Overview

In this role you will build production GenAI systems with ownership beyond the model. You’ll shape patterns, tooling and architecture for GenAI deployment across the organization. You work on RAG systems, AI agents and intelligent workflows, tackling production challenges like evaluation, guardrails and reliability. You’ll implement scalable, observable AI systems using Python and backend engineering, on GCP Vertex AI with Docker and CI/CD pipelines. This is a hands-on, impact-driven opportunity to turn prototypes into trusted AI solutions at scale.

Responsibilities

• Build RAG systems, AI agents, copilots and intelligent document workflows
• Solve production engineering challenges for GenAI deployments
• Implement evaluation, hallucination mitigation and guardrails
• Work with embeddings, vector search and retrieval
• Develop production-grade backend services in Python
• Leverage GCP/Vertex AI, Docker, CI/CD and monitoring
• Create scalable, observable, reliable AI systems for the organization
• Shape patterns, tooling and architecture for GenAI across the company

Key requirements

• experience taking LLM/GenAI applications into production
• strong backend engineering with Python
• familiarity with GCP Vertex AI
• experience with Docker and CI/CD
• practice in embeddings, vector search and retrieval
• focus on production reliability and monitoring
• ownership mindset and ability to shape engineering patterns

• ownership mindset
• problem-solving
• cross-functional collaboration
• LLM/GenAI production experience
• Python backend development
• GCP Vertex AI

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