AI Platform Engineer 2

Position Title:AI Platform Engineer 2
Location:Charlotte, NC (Onsite)
Duration:Long Term Contract
 
Role and responsibilities:
We are seeking an 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.
 
Basic 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

• Design, develop, and deploy AI/ML solutions and generative AI applications for enterprise customers
• Architect and implement solutions leveraging Gemini Enterprise
• Build and productionize ML pipelines, APIs, and services using Python
• Design and implement RAG architectures, agentic workflows, and LLM-based applications
• Deploy, monitor, and optimize models and services on GCP
• 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 and recommend adoption where relevant
• Mentor junior engineers and contribute to best practices for AI/ML development

• 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