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.
Technical Requirements
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Soft Skills
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GenAI Skills
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Experience in LLM models like PaLM, GPT4, Mistral (open-source models),
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Work through the complete lifecycle of Gen AI model development, from training and testing to deployment and performance monitoring.
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Developing and maintaining AI pipelines with multimodalities like text, image, audio etc.
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Have implemented in real-world Chat bots or conversational agents at scale handling different data sources.
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Experience in developing Image generation/translation tools using any of the latent diffusion models like stable diffusion, Instruct pix2pix.
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Expertise in handling large scale structured and unstructured data.
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Efficiently handled large-scale generative AI datasets and outputs.
ML/DL Skills
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High familiarity in the use of DL theory/practices in NLP applications
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Comfort level to code in Huggingface, LangChain, Chainlit, Tensorflow and/or Pytorch, Scikit-learn, Numpy and Pandas
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Comfort level to use two/more of open source NLP modules like SpaCy, TorchText, fastai.text, farm-haystack, and others
NLP Skills
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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.,)
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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
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Familiarity in the use of Docker tools, pipenv/conda/poetry env
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Comfort level in following Python project management best practices (use of setup.py, logging, pytests, relative module imports,sphinx docs,etc.,)
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Familiarity in use of Github (clone, fetch, pull/push,raising issues and PR, etc.,)
Cloud Skills and Computing
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Use of GCP services like BigQuery, Cloud function, Cloud run, Cloud Build, VertexAI,
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Good working knowledge on other open source packages to benchmark and derive summary
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Experience in using GPU/CPU of cloud and on-prem infrastructures
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Skillset to leverage cloud platform for Data Engineering, Big Data and ML needs.
Deployment Skills
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Use of Dockers (experience in experimental docker features, docker-compose, etc.,)
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Familiarity with orchestration tools such as airflow, Kubeflow
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Experience in CI/CD, infrastructure as code tools like terraform etc.
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Kubernetes or any other containerization tool with experience in Helm, Argoworkflow, etc.,
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Ability to develop APIs with compliance, ethical, secure and safe AI tools.
UI
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Good UI skills to visualize and build better applications using Gradio, Dash, Streamlit, React, Django, etc.,
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Deeper understanding of javascript, css, angular, html, etc., is a plus.
Miscellaneous Skills
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Data Engineering:
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Skillsets to perform distributed computing (specifically parallelism and scalability in Data Processing, Modeling and Inferencing through Spark, Dask, RapidsAI or RapidscuDF)
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Ability to build python-based APIs (e.g.: use of FastAPIs/ Flask/ Django for APIs)
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Experience in Elastic Search and Apache Solr is a plus, vector databases.
Responsibilities
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Design NLP/LLM/GenAI applications / products by following robust coding practices,
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Explore SoTA models/techniques so that they can be applied for automotive industry usecases
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Conduct ML experiments to train/infer models; if need be, build models that abide by memory & latency restrictions,
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Deploy REST APIs or a minimalistic UI for NLP applications using Docker and Kubernetes tools
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Showcase NLP/LLM/GenAI applications in the best way possible to users through web frameworks (Dash, Plotly, Streamlit, etc.,)
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Converge multibots into super apps using LLMs with multimodalities
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Develop agentic workflow using Autogen, Agentbuilder, langgraph
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Build modular AI/ML products that could be consumed at scale.
