AI Engineer
Job Location:
Job Location:
Charlotte, VT - USA
Monthly Salary:
Not Disclosed
Monthly Salary:
Posted on:
20 days ago
Posted on:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
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 GCP). Build scalable REST APIs that power LLM-driven applications integrated with enterprise data sources. Implement LLM evaluation frameworks to measure relevance groundedness and hallucination rates. Apply LLMOps/MLOps practices including CI/CD prompt/version management testing and monitoring. Develop systems using embeddings at scale knowledge graphs and ontology extraction. Collaborate across engineering teams and contribute to AI architecture innovation.
Roles and Responsibilities:
Build GenAI applications using foundation models and GraphRAG architectures
Develop autonomous AI agents using modern agentic frameworks
Design and deploy RAG and GenAI services using Python (FastAPI) Docker and cloud platforms
Build scalable REST APIs for LLM-driven enterprise applications
Implement LLM evaluation frameworks (Ragas LangSmith or similar)
Measure and improve model relevance groundedness and hallucination control
Apply LLMOps/MLOps practices including CI/CD pipelines and prompt/version management
Monitor latency cost and response quality in production AI systems
Develop embedding-based systems knowledge graphs and ontology extraction pipelines
Collaborate with engineering teams to deliver scalable AI solutions
Mentor developers and support AI engineering best practices
Build GenAI applications using foundation models and GraphRAG architectures
Develop autonomous AI agents using modern agentic frameworks
Design and deploy RAG and GenAI services using Python (FastAPI) Docker and cloud platforms
Build scalable REST APIs for LLM-driven enterprise applications
Implement LLM evaluation frameworks (Ragas LangSmith or similar)
Measure and improve model relevance groundedness and hallucination control
Apply LLMOps/MLOps practices including CI/CD pipelines and prompt/version management
Monitor latency cost and response quality in production AI systems
Develop embedding-based systems knowledge graphs and ontology extraction pipelines
Collaborate with engineering teams to deliver scalable AI solutions
Mentor developers and support AI engineering best practices
Required Skills:
5 years 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 and ontology extraction
Experience with GraphRAG and advanced RAG implementations
Experience with FastAPI or similar frameworks
Experience deploying AI workloads on AWS Azure or GCP
Strong understanding of LLMOps/MLOps practices
Experience with CI/CD and model evaluation frameworks
Strong problem-solving and communication skills
5 years 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 and ontology extraction
Experience with GraphRAG and advanced RAG implementations
Experience with FastAPI or similar frameworks
Experience deploying AI workloads on AWS Azure or GCP
Strong understanding of LLMOps/MLOps practices
Experience with CI/CD and model evaluation frameworks
Strong problem-solving and communication skills
Preferred Skills:
Full-stack development experience (Python backend modern frontend)
Experience with enterprise-scale AI systems
Experience mentoring or leading engineering teams
Full-stack development experience (Python backend modern frontend)
Experience with enterprise-scale AI systems
Experience mentoring or leading engineering teams
