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