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.