Principal Data Scientist (Multimodal)

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KPI


  • Develop Reusable Components:
    • Objective: Design and implement AI/ML components with reusability in mind.
    • KPI: Ensure that 50% of the developed components are created as reusable components, reducing redundancy and promoting efficiency in future projects.
  • Implement AI as service (AIaS):
    • Objective: Emphasize AI as a service in mind, by creating modular components following best practices.
    • KPI: Achieve 100% AIaS implementation to enhance deployment consistency, reusability, and scalability.

Areas of Responsibility

  • AI solution architecture design and roadmap planning
  • Engineering team leadership and performance management
  • Communication with the customer on the development progress
  • AI solution technical quality and performance management

Qualifications

Skills


  • AI and ML Solutions Architecture: Expertise in AI and ML solutions architecture design, especially in areas like recommender systems, computer vision, NLP, and time series analysis.
  • Advanced Engineering and Data Management: Proficiency in AI and ML solutions engineering, including data handling (data lakes, version control), software engineering (testing, Git, documentation), and model development (TensorFlow, PyTorch).

 

Knowledge


Multi-modal architectures

  • Familiar with the NLP, CV based architectures

 

Data Management:

  • Familiarity with streaming and batch processing concepts.
  • Familiar with Image data, text data and numerical data.

 

Machine Learning Lifecycle:

  • Knowledge of the end-to-end AI/ML workflow and lifecycle.

 

Emerging Technologies:

  • Awareness of emerging technologies and frameworks in machine learning.
  • Familiar with Few-shot learning, RAG or fine-tuning of commercial or open-source models (Mistral/Llama)

 

Experience


  • Building NLP-based systems hands-on, 5+ years
  • Building computer vision algorithms hands-on, 5+ years
  • Optimizing and deploying AL/ML/DL algorithms, 3+ years
  • Development with major cloud providers (AWS, Azure, GCP), 3+ years
  • Leading machine learning teams, 2+ years

We offer

  • Scientific or engineering challenges
  • Work with disruptive deep-tech startups
  • Work with rock stars (senior-level engineers, Ph.D.)
  • Meaningful social and environmental projects
  • Transparent, professional growth plan depending on your impact
  • Remote work from any location
  • Flexible working hours
  • Unpaid unlimited personal time off
  • Regular Team Building & company-wide team events

Felling like a good fit?Apply

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