Data Scientist

ML/DL Skills

High familiarity in the use of DL theory/practices in NLP applicationsComfort level to code in ADK, A2A, AgentSkills, Ontology, Huggingface, LangGraph, LangChain, Chainlit, Tensorflow and/or Pytorch, Scikit-learn, Numpy and PandasComfort 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 envComfort 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 summaryExperience in using GPU/CPU of cloud and on-prem infrastructuresSkillset 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, KubeflowExperience 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 usecasesConduct 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 toolsShowcase 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 multimodalitiesDevelop agentic workflow using Autogen, Agentbuilder, langgraphBuild 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.