AI Platform Engineer

AI Platform Engineer

Work location

Charlotte

AI Engineer

Experience

7+ years

About the Role

We are seeking a AI Engineer to design, build, and scale AI/ML solutions with a focus on enterprise-grade generative AI deployments. This role requires hands-on experience delivering customer-facing AI solutions on Google Cloud Platform, including direct experience with Gemini Enterprise.

Key Responsibilities

Design, develop, and deploy AI/ML solutions and generative AI applications for enterprise customers

Architect and implement solutions leveraging Gemini Enterprise (Gemini for Google Workspace / Gemini Enterprise AI agents, grounding, and search)

Build and productionize ML pipelines, APIs, and services using Python

Design and implement RAG (Retrieval-Augmented Generation) architectures, agentic workflows, and LLM-based applications

Deploy, monitor, and optimize models and services on GCP (Vertex AI, BigQuery, Cloud Run, GKE, Cloud Functions, Pub/Sub)

Collaborate with customers/stakeholders to gather requirements and translate business problems into scalable AI solutions

Ensure solutions meet enterprise standards for security, scalability, performance, and cost efficiency

Partner with data engineering, MLOps, and platform teams to integrate AI capabilities into existing systems

Stay current with evolving GCP AI/ML offerings (Vertex AI, Gemini models, Agent Builder, Model Garden) and recommend adoption where relevant

Mentor junior engineers and contribute to best practices for AI/ML development

Required Qualifications

7+ years of experience in software/AI/ML engineering roles

Hands-on customer-facing experience with Gemini Enterprise (or equivalent enterprise GenAI platforms)

Strong expertise in Google Cloud Platform (GCP) — Vertex AI, BigQuery, Cloud Run/GKE, IAM, networking basics

Advanced proficiency in Python (ML libraries

TensorFlow, PyTorch, scikit-learn, LangChain/LangGraph, etc.)

Solid understanding of LLM fundamentals

prompt engineering, fine-tuning, embeddings, vector databases, RAG

Experience deploying and scaling ML models in production environments

Strong understanding of API design, microservices, and cloud-native architecture

Excellent communication skills — able to engage directly with enterprise customers and stakeholders

Preferred Qualifications

GCP Professional certifications (Machine Learning Engineer, Cloud Architect, or Data Engineer)

Experience with MLOps tooling (Vertex AI Pipelines, Kubeflow, MLflow)

Experience with agentic AI frameworks and multi-agent orchestration

Background in enterprise sales engineering, solutions architecture, or pre-sales technical consulting

Familiarity with data governance, security, and compliance in AI/ML systems