AI Architect (GenAI / MLOps)

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

US Citizen, Green Card, H-1B, OPT-EAD, CPT, Other

Job Title:AI Architect (GenAI / MLOps)
Location:Charlotte, NC
Duration: 8 Months + Extension
Bill Rate: $79/hour (C2C)
Job Type: W-2, 1099, C2C
Client: To Be Discussed Later
Work Authorization: US Citizen, Green Card, OPT-EAD, CPT, H-1B, H-4-EAD, Other

Job Description

Turning promising AI ideas into production systems that are secure, compliant, and built to scale is the hard part of enterprise AI. This role sits at the center of that work. You’ll own the architecture for enterprise AI and GenAI solutions end to end, from data foundations and model selection through deployment, governance, and monitoring. You’ll work closely with business, data, engineering, and risk teams, and you’ll advise executives on what’s feasible, what’s worth it, and what the trade-offs are.

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

Required 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 (e.g., 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

Preferred Qualifications

• Experience in banking, insurance, or asset/wealth management, including regulatory expectations (e.g., SR 11-7 model risk)
• Experience with vector databases (Pinecone, pgvector, OpenSearch) and fine-tuning or evaluation frameworks
• Cloud or architecture certifications (e.g., AWS ML Specialty, Azure AI Engineer, TOGAF)
• Master's or PhD in Computer Science, Data Science, or a related field

We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law.