ML Deployment Engineer Position Available In Mecklenburg, North Carolina
Tallo's Job Summary: The ML Deployment Engineer position involves developing and maintaining machine learning models using GenAI agentic framework, optimizing interactions with large language models, and utilizing techniques like Retrieval-Augmented Generation (RAG). Responsibilities include designing graph databases, fine-tuning Large Language Models (LLMs), and leveraging AWS services for scalable solutions. The role requires data engineering skills, model evaluation, testing, and ongoing performance monitoring and optimization. Security and compliance with Machine Learning Development and Production Lifecycle processes are essential.
Job Description
ML Deployment Engineer
[Malvern, PA, 19355],[Charlotte, NC, 28270],[Malvern, PA, 19355],[Dallas, TX, 75260] | 2025-05-01 10:26:40
Easy Apply
Job Code :
JPC – 7221
Job Description
Key Responsibilities:
Develop, implement, and maintain robust machine learning models using GenAI agentic framework.
Engage in prompt engineering to optimize the interaction with large language models, with other AI systems or tools.
Utilize techniques like Retrieval-Augmented Generation (RAG) to enhance AI solutions with real-time information retrieval.
Develop and design graph databases using tools such as NetworkX or AWS Neptune for relationship-oriented data modeling.
Fine-tune Large Language Models (LLMs) to tailor solutions to specific business needs and improve model efficiency.
Leverage AWS services (including S3, ECS, ECR, Lambda, SageMaker, and more) to build scalable machine learning solutions.
Utilize data engineering skills with Glue and PySpark for efficient data preparation and processing.
Conduct model evaluation and testing to ensure accuracy, reliability, and robustness.
Monitor machine learning models in production, implementing strategies for ongoing performance tracking and optimization.
Ensure the security of models and data through secure API integration, utilizing tokens and comprehensive data security principles.
Govern models in compliance with the Machine Learning Development Lifecycle (MDLC) and Machine Learning Production Lifecycle (MPLC) processes.
