GenAI & MLOps AI Architect for Production-Grade Systems

Artifintech; Charlotte (NC); On-site; USD 90,000 - 128,000; Full time

Job summary

To Be Discussed Later is seeking an AI Architect (GenAI / MLOps) in Charlotte, NC to design and own enterprise AI/GenAI architectures end-to-end, from data foundations to deployment and governance. You will collaborate with business, data, engineering, and risk teams, advising executives on feasible paths and trade-offs.

You will evaluate LLMs, MLOps tooling, and data platforms, champion responsible AI, and lead pilots to production while mentoring engineers and data scientists.

Qualifications

• 6+ years in software or data engineering, with 4+ years architecting AI/ML solutions in production.
• Hands-on experience with LLMs and GenAI: prompt engineering, RAG, embeddings, and agent frameworks (LangChain, LlamaIndex)
• Strong Python skills and fluency in ML frameworks (PyTorch, TensorFlow, scikit-learn)
• Deep experience with at least one major cloud (AWS, Azure, or GCP) and its AI services (e.g., SageMaker, Bedrock, Azure OpenAI, Vertex AI)
• Experience with MLOps tooling (MLflow, Kubeflow, Docker, Kubernetes, CI/CD)
• Solid grounding in data architecture: data lakes/warehouses, streaming, APIs, and microservices
• Knowledge of security, privacy, and governance for AI systems
• Excellent communication, with the ability to explain technical trade-offs to non-technical leaders

Responsibilities

• Design end-to-end AI/ML and GenAI architectures (RAG, agents, fine-tuning, model serving) that integrate with existing enterprise platforms
• Evaluate and select LLMs, vector databases, orchestration frameworks, and MLOps tooling
• Define reference architectures, patterns, and standards for reuse across teams
• Partner with data engineering on pipelines, feature stores, and data quality for AI workloads
• Build in responsible AI: model risk management, explainability, bias testing, audit trails, and regulatory compliance
• Establish MLOps/LLMOps practices covering CI/CD, evaluation, observability, drift detection, and cost control
• Lead proofs of concept and pilots, and move them to production
• Advise stakeholders and executives on AI strategy, feasibility, and trade-offs
• Mentor engineers and data scientists, and run architecture reviews

Skills

Python

GenAI/LLMs

Cloud platforms

Communication

Data architecture

Education

Master’s or PhD in Computer Science / Data Science

Tools

Docker

Kubernetes

MLflow

Kubeflow

CI/CD

Job description

To Be Discussed Later is seeking an AI Architect (GenAI / MLOps) in Charlotte, NC to design and own enterprise AI/GenAI architectures end-to-end, from data foundations to deployment and governance. You will collaborate with business, data, engineering, and risk teams, advising executives on feasible paths and trade-offs.

You will evaluate LLMs, MLOps tooling, and data platforms, champion responsible AI, and lead pilots to production while mentoring engineers and data scientists.