Hybrid Data Science, Gen AI Specialist

at Ford Motor Company

• Job title: Data Science, Gen AI Specialist
• Job type: Full Time
• Experience level: Mid level, Senior
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: Airflow, Angular, Apache, BigQuery, Cloud, Django, Docker, Flask, Google Cloud Platform, JavaScript, Kubernetes, Numpy, Open Source, Pandas, Python, PyTorch, React, Scikit-Learn, Spark, Tensorflow, Terraform
• Location requirements: Hybrid, Chennai, India

About the role

• Data Science/Gen AI Specialist designing applications in NLP/LLM/GenAI for automotive use cases. Collaborating with global teams and working across multiple modalities and data sources.

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.

Requirements

• Bachelor’s or master’s degree in computer science, Engineering, Maths or Science
• Performed any modern NLP/LLM courses/open competitions is also welcomed.
• 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.
• 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.
• 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.
• 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.)
• 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.
• 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.
• 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.
• 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.

Benefits

• Strong communication skills and do excellent teamwork through Git/slack/email/call with multiple team members across geographies.