Generative AI Engineer

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

We are seeking a skilled AI Engineer to join our dynamic team. This role focuses on integrating AI models into production, optimizing machine learning workflows, and creating scalable AI-driven systems. The ideal candidate will have strong experience with Machine Learning Engineering, model evaluation techniques, feedback loop creation, and integrating advanced technologies such as Large Language Models (LLMs).

The AI Engineer will also work on designing and implementing AI orchestration pipelines, with a special emphasis on prompt engineering, vector databases, and embedding strategies for efficient data handling and processing.

Key Responsibilities:

• Machine Learning Engineering:
• Develop, train, and deploy ML models, ensuring they are optimized for production environments.
• Create and maintain automated feedback loops to enhance model accuracy and performance.
• Implement ML pipelines for continuous evaluation and refinement of models in production.
• AI Orchestration & Integration:
• Integrate Large Language Models (LLMs) into business applications.
• Build AI orchestration systems to manage the end-to-end lifecycle of AI models, including deployment and scaling.
• Work with Vector Databases (Vector DB) to store and query high-dimensional data for AI applications.
• Set up evaluation metrics and processes to assess model performance over time.
• Create feedback loops using real-world data to improve model reliability and accuracy.
• Develop GenAI-driven Text-to-SQL solutions to automate database queries based on natural language input.
• Optimize GenAI workflows for database interactions and information retrieval.
• Embedding/Chunking & Prompt Engineering:
• Design and implement embedding and chunking strategies for scalable data processing.
• Utilize prompt engineering techniques to fine-tune the performance of AI models in production environments.

Required Qualifications:

• Bachelor's or master's degree in computer science, AI, Machine Learning, or a related field.
• Proven experience in building, deploying, and maintaining ML models in production environments.
• Proficiency in programming languages like Python, and frameworks such as TensorFlow, PyTorch, or similar.
• Familiarity with LLMs, Vector DB, embedding/chunking strategies, and AI orchestration tools.
• Strong understanding of model evaluation techniques and feedback loop systems.
• Hands-on experience with Text-to-SQL and prompt engineering methodologies.
• Knowledge of cloud platforms (AWS) and containerization tools (Docker, Kubernetes).