Opportunity brief
Ford is hiring for the role of Data Scientist!
Responsibilities of the Candidate:
ML/DL Skills:
- High familiarity in the use of DL theory/practices in NLP applications - Comfort level to code in ADK, A2A, AgentSkills, Ontology, Huggingface, LangGraph, 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.
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. - 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.
- Strong communication skills and do excellent teamwork through Git/slack/email/call with multiple team members across geographies.
- Experience in LLM models like GPT5, Gemini, Kimi, Seedance (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.