AI Architect (GenAI / MLOps)

Charlotte, NC (Hybrid)

Full Time 

Position Overview

Design and lead enterprise AI/ML and Generative AI solutions from architecture through production deployment, ensuring scalability, security, compliance, and governance. Advise business leaders and mentor engineering teams on AI strategy and implementation.

Key Responsibilities

• Design end-to-end AI/GenAI architectures, including RAG, AI agents, fine-tuning, and model serving.

• Evaluate LLMs, vector databases, orchestration frameworks, and MLOps tools.

• Define reusable AI architecture standards and reference designs.

• Collaborate on data pipelines, feature stores, and data quality.

• Implement responsible AI, model governance, security, explainability, and compliance.

• Establish MLOps/LLMOps practices for CI/CD, monitoring, model evaluation, drift detection, and cost optimization.

• Lead AI proofs of concept and production deployments.

• Advise executives on AI strategy, feasibility, and technical trade-offs.

• Mentor engineers and data scientists and lead architecture reviews.

Required Qualifications

• 10 years in software/data engineering, including 4 years architecting production AI/ML solutions.

• Hands-on expertise in LLMs, GenAI, prompt engineering, RAG, embeddings, and agent frameworks (LangChain/LlamaIndex).

• Strong Python skills and experience with PyTorch, TensorFlow, or scikit-learn.

• Experience with AWS, Azure, or Google Cloud Platform AI services (SageMaker, Bedrock, Azure OpenAI, or Vertex AI).

• MLOps experience with MLflow, Kubeflow, Docker, Kubernetes, and CI/CD.

• Strong understanding of data architecture, data lakes/warehouses, streaming, APIs, and microservices.

• Knowledge of AI security, privacy, governance, and regulatory compliance.

• Excellent communication and stakeholder management skills.

Preferred Qualifications

• Banking, insurance, or wealth management experience; familiarity with SR 11-7 model risk guidelines.

• Experience with Pinecone, pgvector, OpenSearch, fine-tuning, and model evaluation.

• Cloud/architecture certifications such as AWS ML Specialty, Azure AI Engineer, or TOGAF.

• Master’s or PhD in Computer Science, Data Science, or a related field.