AI Engineer – Agentic AI

Location: Charlotte, NC – Onsite
Experience: 7+ Years

Job Overview

We are seeking an experienced AI Engineer – Agentic AI to design, build, and scale AI/ML and enterprise-grade Generative AI solutions. The ideal candidate will have strong hands-on experience with GCP, Gemini Enterprise, Vertex AI, Python, RAG, Agentic AI, LLM applications, and production ML deployments.

Key Responsibilities

• Design, develop, and deploy AI/ML and Generative AI solutions for enterprise customers.
• Architect solutions leveraging Gemini Enterprise, including AI agents, grounding, search, and Gemini for Google Workspace.
• 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 AI/ML models and services on GCP.
• Work with Vertex AI, BigQuery, Cloud Run, GKE, Cloud Functions, and Pub/Sub.
• Gather requirements from customers and stakeholders and translate business problems into scalable AI solutions.
• Ensure AI solutions meet enterprise requirements for security, scalability, performance, and cost efficiency.
• Collaborate with Data Engineering, MLOps, and Platform teams to integrate AI capabilities into existing systems.
• Stay current with Vertex AI, Gemini models, Agent Builder, and Model Garden.
• Mentor junior engineers and contribute to AI/ML development best practices.

Required Skills

• 7+ years of experience in software, AI, or ML engineering.
• Hands-on customer-facing experience with Gemini Enterprise or equivalent enterprise GenAI platforms.
• Strong expertise in Google Cloud Platform (GCP).
• Experience with:
  • Vertex AI
  • BigQuery
  • Cloud Run / GKE
  • IAM
  • GCP networking fundamentals
• Advanced Python programming skills.
• Experience with ML/AI frameworks such as:
  • TensorFlow
  • PyTorch
  • Scikit-learn
  • LangChain
  • LangGraph
• Strong understanding of LLM fundamentals, including:
  • Prompt Engineering
  • Embeddings
  • Fine-tuning
  • Vector Databases
  • RAG
• Experience deploying and scaling ML models in production.
• Strong understanding of API design, microservices, and cloud-native architecture.
• Excellent communication and customer-facing skills.

Preferred Qualifications

• GCP Professional certifications such as:
  • Professional Machine Learning Engineer
  • Professional Cloud Architect
  • Professional Data Engineer
• Experience with MLOps tools:
  • Vertex AI Pipelines
  • Kubeflow
  • MLflow
• Experience with Agentic AI frameworks and multi-agent orchestration.
• Background in Solutions Architecture, Sales Engineering, or Pre-Sales Technical Consulting.
• Knowledge of AI governance, security, and compliance.
• Enterprise customer engagement experience.

Good to Have

• BigQuery
• Vector Search
• MLOps
• GKE / Kubernetes
• Docker
• APIs / Microservices
• Data Engineering
• CI/CD
• Terraform
• Gemini / Vertex AI
• Agent Builder / Model Garden

Core Skills

AI/ML | Generative AI | Agentic AI | Gemini Enterprise | GCP | Vertex AI | Python | RAG | LLM | LangChain | LangGraph | Vector Databases | BigQuery | Cloud Run | GKE | Kubernetes | ML Pipelines | MLOps | APIs | Microservices | Docker | Terraform | CI/CD | AI Architecture