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, H4-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