Staff ML Engineer (LLM)

We are seeking a Staff ML Engineer with a focus on productizing LLM and building Agents to join our team. This role involves designing, developing and working with structured and semi-structured data to build an overall Agentic framework capable of completing a set of tasks to complete user requests. The ideal candidate will have extensive (4-6 years') experience in Natural Language Processing (NLP), Machine Learning/Deep Learning, MLOps, understanding of LLMs and a history of successful deployments of Deep Learning or Machine Learning solutions. Responsibilities: Assist in architecting Agent roles and functionalities for the backend application. Conduct research with other team members to evaluate and determine which LLM to utilize for different Agents. Create and refine conversation prompts to refine Agent capabilities with few-shot examples. Participate in code reviews and provide constructive feedback to team members. Mentor team members and provide technical guidance Stay up to date with topics like Agentic AI, LLMs and Deep Learning to help improve our product. Requirements: Bachelor's degree in computer science, engineering, or a related field Proven 3 years of experience as a Machine Learning Engineer (MLE)/Sr. MLE or similar roles. Proficiency in Python and SQL is required, plus knowledge of one or more programming languages such as Java, C++ or JavaScript. Proficient in PyTorch/Tensorflow, NumPy and Pandas. Demonstratable experience in using Hugging Face models and Transformers library for custom application deployment, via finetuning or prompt engineering. Experience working with various cloud providers such as AWS, GCP or Azure to deploy and monitor Deep Learning/Generative AI products. A solid understanding of Git, Kubernetes, Redis, Kafka and PostgreSQL Excellent communication, interpersonal skills and willingness to go the extra mile to build a great product. Preferred Qualifications Master’s degree or above in computer science, engineering or related field. Experience building Agentic AI applications with open-source models such as BERT, QWEN or LLAMA. Knowledge of linguistics or cognitive science. Publications or contributions to the field of deep learning, or NLP.