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AI Engineer

Job

Trail Blazer Consulting LLC

Charlotte, NC (In Person)

Full-Time

Posted 3 days ago (Updated 18 hours ago) • Actively hiring

Expires 7/24/2026

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Job Description

Job Title:
AI Engineer Location:
Onsite:
Dallas, TX or Charlotte, NC, Duration:
6 months
Interview :
video then onsite
Dallas or Charlotte Job Description:
Process:
TEAMS with me for 10 min, (no Glider), 10 min with HR to ensure everyone is real and not reading ChatGPT then 1-2 interviews with the AI Team at CAPCO/Wells. Candidate have to be all 3 of these bullets, Deploying AI models into live production environment Integrating models into real applications or system Supporting models post-deployment (monitoring, performance, scalability, etc.) Wells Fargo - AI Engineer - GenAI /
Agentic System Location:
Dallas, TX or Charlotte, NC Work Arrangement:
On-site, 5 days per week What You''ll Do Build GenAI applications leveraging foundation models and advanced architectures such as GraphRAG. Develop autonomous AI agents using modern agentic frameworks. Design and deploy RAG and GenAI services using Python (FastAPI), Docker, and cloud platforms (AWS, Azure, or Google Cloud Platform). Build scalable REST APIs that power LLM-driven applications integrated with enterprise data sources. Implement LLM evaluation frameworks using tools such as Ragas, LangSmith, or custom benchmarks to measure answer relevance, groundedness, and hallucination rates. Apply LLMOps/MLOps practices, including CI/CD pipelines, prompt/version management, automated testing, and monitoring of latency, cost, and response quality. Develop systems leveraging embeddings at scale, knowledge graphs, and ontology extraction. Collaborate across engineering teams, mentor developers, and help drive innovation in GraphRAG and agentic AI architectures. ________________________________________ What You''ll Bring Degree in Computer Science, AI/ML, or related field. 5+ years of AI/ML-focused software engineering experience. Production experience building LLM-based or agentic AI systems. Strong expertise in Python and modern AI frameworks. Experience with embeddings, knowledge graphs, ontology extraction, and advanced RAG/GraphRAG implementations. Full-stack development experience (Python back end + modern front end). Experience deploying AI workloads to AWS, Azure, or Google Cloud Platform. Familiarity with LLMOps/MLOps tooling and model evaluation frameworks. Strong problem-solving, communication, and collaboration skills.