GenAI Specialist

You have expertise in handling large scale structured and unstructured data efficiently. You are familiar with the use of Docker tools, pipenv/conda/poetry env, and comfortable following Python project management best practices. You have a high familiarity with DL theory/practices in NLP applications and can code in various frameworks such as Huggingface, LangChain, Chainlit, Tensorflow, Pytorch, Scikit-learn, Numpy, and Pandas. Additionally, you are comfortable using open source NLP modules like SpaCy, TorchText, fastai.text, farm-haystack, and others. Your knowledge includes fundamental text data processing, such as regex, token/word analysis, spelling correction, noise reduction in text, and deriving insights from clustering. You have experience implementing real-world BERT or other transformer fine-tuned models, from data preparation to deployment. You have also utilized GCP services like BigQuery, Cloud function, Cloud run, Cloud Build, and VertexAI, along with working knowledge on other open-source packages. Your educational background includes a Bachelors in Engineering or Masters Degree in Computer Science, Engineering, Maths, or Science. Any experience in modern NLP/LLM courses/open competitions is welcomed. In this role, you will design NLP/LLM/GenAI applications/products following robust coding practices. You will explore SoTA models/techniques applicable for automotive industry use cases and conduct ML experiments to train/infer models. If needed, you will build models that comply with memory and latency restrictions. Your responsibilities also include deploying REST APIs or a minimalistic UI for NLP applications using Docker and Kubernetes tools. You will showcase NLP/LLM/GenAI applications effectively to users through web frameworks like Dash, Plotly, Streamlit, etc. Furthermore, you will converge multibots into super apps using LLMs with multimodalities and develop agentic workflow using Autogen, Agentbuilder, langgraph. Lastly, you will build modular AI/ML products that can be consumed at scale.,