GenAI Specialist

Ford Artificial Intelligence Advancement Center is looking for professionals experienced in NLP/LLM/GenAI, who are hands-on and can employ many NLP/Prompt engineering techniques from traditional statistical/ML NLP to DL-based sequence models and transformers in their day-to-day work. 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. Qualifications: Education : Bachelors or master's degree in computer science, Engineering, Maths or Science Performed any modern NLP/LLM courses/open competitions is also welcomed. Technical Requirements : Soft Skills : - Strong communication skills and do excellent teamwork through Git/slack/email/call with multiple team members across geographies. 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 NLP Skills : - Knowledge in fundamental text data processing (like use of regex, token/word analysis, spelling correction/noise reduction in text, segmenting noisy unfamiliar sentences/phrases at right places, deriving insights from clustering, etc.,) - Have implemented in real-world BERT/or other transformer fine-tuned models (Seq classification, NER or QA) from data preparation, model creation and inference till deployment. Python Project Management Skills - Familiarity in the use of Docker tools, pipenv/conda/poetry env - Comfort level in following Python project management best practices (use of setup.py, logging, pytests, relative module imports,sphinx docs,etc.,) - Familiarity in use of Github (clone, fetch, pull/push,raising issues and PR, etc.,) Cloud Skills and Computing : - Use of GCP services like BigQuery, Cloud function, Cloud run, Cloud Build, VertexAI, - Good working knowledge on other open-source packages to benchmark and derive summary. - Experience in using GPU/CPU of cloud and on-prem infrastructures. - Skillset to leverage cloud platform for Data Engineering, Big Data and ML needs. Deployment Skills : - Use of Dockers (experience in experimental docker features, docker-compose, etc.,) - Familiarity with orchestration tools such as airflow, Kubeflow - Experience in CI/CD, infrastructure as code tools like terraform etc. - Kubernetes or any other containerization tool with experience in Helm, Argoworkflow, etc., - Ability to develop APIs with compliance, ethical, secure and safe AI tools. UI : - Good UI skills to visualize and build better applications using Gradio, Dash, Streamlit, React, Django, etc., - Deeper understanding of javascript, css, angular, html, etc., is a plus. Miscellaneous Skills : Data Engineering: - Skillsets to perform distributed computing (specifically parallelism and scalability in Data Processing, Modeling and Inferencing through Spark, Dask, RapidsAI or RapidscuDF) - Ability to build python-based APIs (e.g.: use of FastAPIs/ Flask/ Django for APIs) - Experience in Elastic Search and Apache Solr is a plus, vector databases. Skills: Nlp, Tensorflow, Pytorch, Llm Experience: 5.00-10.00 Years