Company Name

Ford ( Bangalore )

Job Description

• You'll be working alongside leading technical experts from all around the world, on a variety of products involving Sequence/token classification, QA/chatbots, translation, semantic/search and summarization, among others.
  Responsibilities:
  • Design NLP/LLM/GenAI applications/products by following robust coding practices,
  • Explore SoTA models/techniques so that they can be applied for automotive industry usecases
  • Conduct ML experiments to train/infer models; if need be, build models that abide by memory & latency restrictions,
  • Deploy REST APIs or a minimalistic UI for NLP applications using Docker and Kubernetes tools.
  • Showcase NLP/LLM/GenAI applications in the best way possible to users through web frameworks (Dash, Plotly, Streamlit, etc.,)
  • Converge multibots into super apps using LLMs with multimodalities.
  • Develop agentic workflow using Autogen, Agentbuilder, langgraph
  • Build modular AI/ML products that could be consumed at scale.
  GenAI Skills:
  • Experience in LLM models like PaLM, GPT4, Mistral (open-source models),
  • Work through the complete lifecycle of Gen AI model development, from training and testing to deployment and performance monitoring.
  • Developing and maintaining AI pipelines with multimodalities like text, image, audio etc.
  • Have implemented in real-world Chat bots or conversational agents at scale handling different data sources.
  • Experience in developing Image generation/translation tools using any of the latent diffusion models like stable diffusion, Instruct pix2pix.
  • Expertise in handling large scale structured and unstructured data.
  • Efficiently handled large-scale generative AI datasets and outputs.
  ML/DL Skills:
  • High familiarity in the use of DL theory/practices in NLP applications
  • Comfort level to code in Huggingface, LangChain, Chainlit, Tensorflow and/or Pytorch, Scikit-learn, Numpy and Pandas
  • Comfort level to use two/more of open source NLP modules like SpaCy, TorchText, fastai.text, farm-haystack, and others

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