AI Engineer

AI Engineer

Location: Charlotte, NC – Hybrid

About the Role

We are seeking an experienced AI Engineer to design, build, and scale AI/ML solutions with a focus on enterprise-grade Generative AI deployments. The ideal candidate will have hands-on experience delivering customer-facing AI solutions on Google Cloud Platform (GCP), including 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, including Gemini for Google Workspace, 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 AI/ML models and services on GCP.
• Work with Vertex AI, BigQuery, Cloud Run, GKE, Cloud Functions, and Pub/Sub.
• Collaborate directly with customers and stakeholders to gather requirements and translate business problems into scalable AI solutions.
• Ensure AI 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 enterprise systems.
• Stay current with evolving GCP AI/ML technologies, including Vertex AI, Gemini models, Agent Builder, and Model Garden.
• Mentor junior engineers and contribute to AI/ML development standards and best practices.

Required Qualifications

• 7+ years of experience in software, AI, or ML engineering roles.
• Hands-on customer-facing experience with Gemini Enterprise or equivalent enterprise Generative AI platforms.
• Strong expertise in Google Cloud Platform (GCP), including: Vertex AI, BigQuery, Cloud Run / GKE, IAM, Basic networking.
• Advanced Python programming skills.
• Experience with ML/AI libraries and frameworks such as TensorFlow, PyTorch, Scikit-learn, LangChain, LangGraph.
• Strong understanding of LLM fundamentals, including 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 with the ability to work directly with enterprise customers and stakeholders.

Preferred Qualifications

• GCP Professional certifications such as Professional Machine Learning Engineer, Professional Cloud Architect, Professional Data Engineer.
• Experience with MLOps tools such as Vertex AI Pipelines, Kubeflow, and MLflow.
• Experience with Agentic AI frameworks and multi-agent orchestration.
• Background in Sales Engineering, Solutions Architecture, or Pre-Sales Technical Consulting.
• Familiarity with AI data governance, security, compliance, and responsible AI practices.

Key Skills

AI/ML | Generative AI | GCP | Vertex AI | Gemini Enterprise | Gemini Models | Python | TensorFlow | PyTorch | Scikit-learn | LangChain | LangGraph | LLM | RAG | Prompt Engineering | Embeddings | Vector Databases | Agentic AI | Agent Builder | Model Garden | BigQuery | Cloud Run | GKE | Cloud Functions | Pub/Sub | API Design | Microservices | MLOps | MLflow | Kubeflow

Top 3 Must-Have Skills

1. Gemini Enterprise / Enterprise Generative AI
2. GCP / Vertex AI / Python
3. RAG, LLMs & Agentic AI