AI Data Scientist
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Job Location:
Monthly Salary:
Posted on:
Yesterday
Vacancies:
1 Vacancy
Job Summary
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.
Responsibilities
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.
Qualifications
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.
Required Experience:
IC
Key Skills
- Laboratory Experience
- Immunoassays
- Machine Learning
- Biochemistry
- Assays
- Research Experience
- Spectroscopy
- Research & Development
- cGMP
- Cell Culture
- Molecular Biology
- Data Analysis Skills
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