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Lead R&D

Lead R&D AI/ML engineer is hands-on technical leaders who enjoy

  • Designing and implementing data solutions best-suited to deliver on our customer needs and use cases — from streaming, to data lakes, to analytics, and beyond across a progressively evolving technical stack.
  • Providing thought leadership by recommending the right technologies and solutions for a given use case, from the application layer to infrastructure; and have the team leadership and coding skills (e.g. Python, Java, and Scala) to get their solutions into production — and to help ensure performance, security, scalability, and robust data integration.
  • Driving Customer success in a collaborative way while mentoring InData Labs data engineers.
  • Switching between creating high level solutions and implementing those doing active development work.
  • Enjoy working seamlessly across a variety of technical stacks

Required Experience:

  • Proven experience as a technical team lead, and/or mentorship of other engineers
  • Ability to develop end-to-end technical solutions
  • Programming Expertise in Java, Python, Scala, or another modern programming language
  • Strong working knowledge of SQL and the ability to write, debug, and optimize SQL queries
  • Client-facing written and verbal communication skills and experience
  • Create and deliver detailed presentations
  • Detailed solution documentation (e.g. sequence diagrams, class hierarchies, logical system views, etc.)
  • Establish scalable, efficient, and automated processes for large scale ML model deployments
  • 4-year Bachelor’s degree in Computer Science or a related field

Prefer any of the following:

  • Languages:
    • Python,  С++ (would be nice)
    • SQL, Gremlin (Cypher)
  • Frameworks and libraries:
    • TensorFlow, TensorRT.
    • PyTorch.
    • Scikit-learn
    • NumPy
    • Pandas.
    • Jupyter Notebook.
    • PySpark, Dask, Polars
    • Flask/FastAPI
  • Would be nice – understanding how to work with Edge/Mobile devices (limitations, AI libraries)
  • Tools
    • Docker
    • Kubernetes
    • SageMaker
    • Google Colab
    • Git.
  • Clouds:
    • AWS/GCP/Azure (Serverless architecture, AI/ML services, deployment)
  • Databases, storages:
    • SQL (PostgreSQL/MySQL).
    • Non-SQL (MongoDB, Apache Kafka, Neo4j, AWS Neptune, Cassandra)
    • DWH (Snowflake, Redshift)
    • Apache Hadoop.
  • Data Visualization:
    • Matplotlib.
    • Seaborn.
    • Plotly.
    • Tableau.
  • Large Language Models (LLM):
    • Transformers ( GPT, BERT, RoBERTa etc.).
    • Hugging Face.
    • PyTorch-Transformers, TensorFlow-Transformers.
    • LLM Fine-tuning, Multi-GPU training
  • Other:
    • MLFlow
  • Would be nice:
    • Experience in Non-GPU (TPU/IPU) solutions
  • Expertise/skills:
    • NLP (classical + DL)
    • Research, fast prototyping, documenting
    • MLOps
    • NN porting

Ideal opportunity if you enjoy

  • Thinking independently and seeing the big picture
  • Translating customer needs into solutions
  • Confidently standing behind your recommendations
  • Exploring new technologies in an effort to always grow and uncover better ways to solve problems (for instance: automating all the things)
  • Explaining your technical decisions, both internally and externally
  • Smiling in the face of complex problems
  • Collaborating and helping your fellow data engineers learn and grow