Job Overview

Singapore AI-Hub at TE is developing various applications/tools to support integration of AI in product development process. We are on an exciting journey to build and scale AI-Hub team.

Job Responsibilities

• Use ML, deep learning and Generative AI tools and other technologies to design, evangelize, and implement state-of-the-art solutions.
• Define and implement best practices for building, testing, and deploying scalable AI solutions, with a focus on generative models and LLMs using proprietary provided models or open-source models.
• Drive successful business outcomes by designing and building cloud hosted Generative AI solutions
• Implement strategies for efficient and effective training of LLMs to achieve optimal performance
• Work closely with Internal teams to integrate RAG workflows into their applications and systems and stay abreast of the latest developments in language models and generative AI technologies.
• Experience with a public cloud AWS and on-premises setup. Relevant experience that showcases practical understanding of LLMs, Prompting, Fine- tuning, Vector DBs, Knowledge graph (Neo4J), Architectural Design for Information Retrieval using RAG
• Work in evaluating, building and fine-tuning different ML models, and LLMs to solve the problem.
• Mentor team members and enable technical decision-making.

Job Requirements

• Master's or Ph.D. in Computer Science, Artificial Intelligence, or equivalent experience
• 2+ years of hands-on experience in a technical role, specifically focusing on generative AI, with a strong emphasis on training Large Language Models (LLMs).
• Proven track record of successfully deploying and optimizing LLM models for inference in production environments.
• Having experience working with LLM/RAG/FineTuning, Amazon Bedrock/Sagemaker JumpStart
• Expert as NLP techniques. Should have worked with deep learning libraries (Transformers based models, LSTM, biLSTM, CNN, etc)
• Experience with other Machine Learning algorithms and tools (scikit-learn libraries, Pandas, Numpy, Tensorflow, PyTorch).
• Fluency in at least two programming languages Python, JavaScript/Typescript, NodeJS (Python preferred)
• Deep understanding of data structures and algorithms.
• Expertise in training and fine-tuning LLMs using popular frameworks such as TensorFlow, PyTorch, or Hugging Face Transformers.
• Excellent communication and collaboration skills with the ability to articulate complex technical concepts to both technical and non-technical stakeholders.
• Hands-on experience in MLOps/ LLMOps including data collection, data preparation, model training/ refinement, model validation, drift management and model serving.
• Experience leading workshops, training sessions, and presenting technical solutions to diverse audiences.
• Knowledge of  Open-source Models, Transformer Models, Encoder-decoder architecture, Latent Spaces, and LLM orchestration frameworks (e.g.LangChain, LlamaIndex), Vector Database)

Competencies