Data Scientist

3 days ago

Chennai Tamil Nadu, India Ford Motor Company Full time

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.,)
- 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
- 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.
- Deeper understanding of javascript, css, angular, html, etc., is a plus.

**Education**: Bachelor’s or Master’s Degree in Computer Science, Engineering, Maths or Science

Performed any modern NLP/LLM courses/open competitions is also welcomed.
- 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,
- 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.

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.

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