GenAI Engineer: Production ML & LLM Orchestration

VBeyond Corporation; New York (NY); On-site; USD 140,000 - 210,000; Full time

Job summary

VBeyond Corporation is seeking an AI Engineer to integrate AI models into production, optimize ML workflows, and build scalable AI systems. You will focus on production-grade ML pipelines, model evaluation, and continuous improvement.

The role emphasizes AI orchestration, LLM integration, vector databases, embedding strategies, prompt engineering, and GenAI-enabled data querying to power enterprise applications.

Qualifications

• Bachelor's or master's degree in computer science, AI, ML, or related field.
• Proven experience building, deploying, maintaining ML models in production.
• Proficiency in Python and ML frameworks (TensorFlow, PyTorch).
• Familiarity with LLMs, Vector DBs, embeddings, and AI orchestration tools.
• Strong understanding of model evaluation techniques and feedback loops.
• Hands-on Text-to-SQL and prompt engineering experience.
• Knowledge of AWS and containerization (Docker, Kubernetes).

Responsibilities

• Develop, train, and deploy ML models for production.
• Create and maintain automated feedback loops to improve accuracy.
• Implement ML pipelines for continuous evaluation and refinement.
• Integrate LLMs into business applications and orchestrate AI lifecycles.
• Work with vector databases to manage high-dimensional data.
• Set up metrics to monitor model performance over time.
• Design embedding/chunking strategies for scalable data processing.
• Apply prompt engineering to optimize AI models in production.

Skills

Python programming

LLMs

Vector databases

Embedding/chunking

Prompt engineering

Model evaluation

AI orchestration

Cloud platforms

Education

Bachelor's or Master's in CS/AI/ML

Tools

TensorFlow

PyTorch

Docker

Kubernetes

AWS

Job description

VBeyond Corporation is seeking an AI Engineer to integrate AI models into production, optimize ML workflows, and build scalable AI systems. You will focus on production-grade ML pipelines, model evaluation, and continuous improvement.

The role emphasizes AI orchestration, LLM integration, vector databases, embedding strategies, prompt engineering, and GenAI-enabled data querying to power enterprise applications.