Position Overview

As a Machine Learning Engineer at S Management, you will play a crucial role in designing, developing, (and deploying) AI solutions that leverage Large Language Models. You will work closely on building applications such as Retrieval-Augmented Generation (RAG), on fine-tuning models for specific use cases, and integrating these models into (our) products and services.

Key Responsibilities

● Model Fine-Tuning: Fine-tune pre-trained language models to enhance performance on specific tasks and datasets.
● Integration and Implementation: Facilitate integrating LLM-based solutions into existing products and services.
● Research and Innovation: Stay updated with the latest advancements in AI and machine learning, and experiment with new techniques to continuously improve a solution.
● Performance Optimization: Monitor and optimise the performance of deployed solutions, ensuring scalability, efficiency, and robustness.
● Data Collection: Collecting and parsing data for training and evaluation of language models.

Qualifications

Educational Background:

– Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field.

Technical Skills:

– Proficiency in Python and machine learning frameworks such as TensorFlow, PyTorch or Jax.
– Experience with data collection and preprocessing, feature engineering, and data pipeline development.
– Knowledge of natural language processing (NLP) techniques.
– Experience working with Large Language Models (e.g., GPT, Llama) and building and deploying applications on top of these models.
– Proficiency with NLP frameworks like NLTK, Hugging Face, LangChain, or vLLM.
– Familiarity with integrating AI models into production environments, including API development and deployment strategies.

Good-to-have Experience:

– Experience with data collection for evaluating or training Large Language Models.
– Experience working with business leaders, subject matter experience, end users, and other stakeholders to define and analyze solution requirements.
– Experience leading software engineering, data analysis, or product development.
– Experience with fine-tuning or training Large Language Models.
– Practical experience training models with TPUs or multiple GPUs.
– Practical experience with OpenAI’s API or similar.
– Practical experience in building and deploying Retrieval-Augmented Generation (RAG) systems.
– Familiarity with cloud platforms such as AWS, Google Cloud, or Azure for deploying and managing AI solutions.