Data ScienceGen AI Specialist

Youll be working alongside leading technical experts from all around the world on a variety of products involving Sequence/token classification QA/chatbots translation semantic/search and summarization among others.

• 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: Bachelors or masters degree in computer science Engineering Maths or Science

Performed any modern NLP/LLM courses/open competitions is also welcomed.

Technical Requirements:

Soft Skills:

• Strong communication skills and do excellent teamwork through Git/slack/email/call with multiple team members across geographies.

GenAI Skills:

• Experience in LLM models like PaLM GPT4 Mistral (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.

ML/DL Skills:

• High familiarity in the use of DL theory/practices in NLP applications
• Comfort level to code in Huggingface 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 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 logging pytests relative module importssphinx docsetc.)
• Familiarity in use of Github (clone fetch pull/pushraising 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.

Miscellaneous Skills:

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.