V
Generative AI Engineer
Virtual Vocations Inc
2 hours ago
Full-time
On-site
New York City, New York, United States
To support the development of advanced AI solutions, a contract Generative AI Engineer specializing in LLM and agentic systems will design, develop, and deploy production-grade applications remotely over a 6-month period.
Key responsibilities
Design and implement production-grade Generative AI applications using LLMs and multi-agent architectures Build RAG pipelines and agent workflows while integrating tools using frameworks like LangGraph and LangChain Develop scalable backend services and user-facing applications utilizing Python, TypeScript, FastAPI, and React
Required qualifications
Bachelor's degree in Computer Science, Software Engineering, Information Technology, or a related field, or equivalent practical experience Proven experience in developing and deploying production-grade Generative AI applications and enterprise LLM solutions Expertise in Python, TypeScript, FastAPI, React, and REST API development Strong understanding of AWS and GCP cloud environments, along with scalable multi-tenant applications Experience with AI evaluation frameworks, observability, and CI/CD practices
Design and implement production-grade Generative AI applications using LLMs and multi-agent architectures Build RAG pipelines and agent workflows while integrating tools using frameworks like LangGraph and LangChain Develop scalable backend services and user-facing applications utilizing Python, TypeScript, FastAPI, and React
Required qualifications
Bachelor's degree in Computer Science, Software Engineering, Information Technology, or a related field, or equivalent practical experience Proven experience in developing and deploying production-grade Generative AI applications and enterprise LLM solutions Expertise in Python, TypeScript, FastAPI, React, and REST API development Strong understanding of AWS and GCP cloud environments, along with scalable multi-tenant applications Experience with AI evaluation frameworks, observability, and CI/CD practices