At TE we strongly believe that data and AI are strategic drivers for future success. We are building a world class advanced analytics team that will solve some of the most complex strategic problems and deliver topline growth and operational efficiencies across our business units.
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)
