About this role
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.