Ai Data Scientist

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

erformed any modern NLP/LLM courses/open competitions is also welcomed.
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**echnical Requirements**:
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**oft Skills**: SG

**enAI Skills**: E- xperience in LLM models like PaLM, GPT4, Mistral (open-source models), W- ork through the complete lifecycle of Gen AI model development, from training and testing to deployment and performance monitoring. D- eveloping and maintaining AI pipelines with multimodalities like text, image, audio etc. H- ave implemented in real-world Chat bots or conversational agents at scale handling different data sources. E- xperience in developing Image generation/translation tools using any of the latent diffusion models like stable diffusion, Instruct pix2pix. E- xpertise in handling large scale structured and unstructured data. E- fficiently handled large-scale generative AI datasets and outputs.
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**L/DL Skills**: H-
- omfort level to code in Huggingface, LangChain, Chainlit, Tensorflow and/or Pytorch, Scikit-learn, Numpy and Pandas C- omfort level to use two/more of open source NLP modules like SpaCy, TorchText, fastai.text, farm-haystack, and others
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**LP Skills**: K- nowledge 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.,) H- ave 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
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**ython Project Management Skills F**- amiliarity in the use of Docker tools, pipenv/conda/poetry env C- omfort level in following Python project management best practices (use of setup.py, logging, pytests, relative module imports,sphinx docs,etc.,) F- amiliarity in use of Github (clone, fetch, pull/push,raising issues and PR, etc.,)
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**loud Skills and Computing**: U- se of GCP services like BigQuery, Cloud function, Cloud run, Cloud Build, VertexAI, G- ood working knowledge on other open source packages to benchmark and derive summary E- xperience in using GPU/CPU of cloud and on-prem infrastructures S- killset to leverage cloud platform for Data Engineering, Big Data and ML needs.
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**eployment Skills**: U- se of Dockers (experience in experimental docker features, docker-compose, etc.,) F- amiliarity with orchestration tools such as airflow, Kubeflow E- xperience in CI/CD, infrastructure as code tools like terraform etc. K- ubernetes or any other containerization tool with experience in Helm, Argoworkflow, etc., A- bility to develop APIs with compliance, ethical, secure and safe AI tools.
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**I**: G-
- eeper understanding of javascript, css, angular, html, etc., is a plus.
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**iscellaneous Skills**: D

ata Engineering: S- killsets to perform distributed computing (specifically parallelism and scalability in Data Processing, Modeling and Inferencing through Spark, Dask, RapidsAI or RapidscuDF) A- bility to build python-based APIs (e.g.: use of FastAPIs/ Flask/ Django for APIs) E- xperience in Elastic Search and Apache Solr is a plus, vector databases.

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**esponsibilities**: D-
- xplore SoTA models/techniques so that they can be applied for automotive industry usecases C- onduct ML experiments to train/infer models; if need be, build models that abide by memory & latency restrictions, D- onverge multibots into super apps using LLMs with multimodalities D- evelop agentic workflow using Autogen, Agentbuilder, langgraph B