Data Scientist

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.

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. 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.