Job description 

In one sentence

This is a hands-on position for a motivated and talented innovator. The Data Scientist performs data mining and develops algorithms that provide insight from data.

What will your job look like?

Machine Learning Engineer Job Description:
Development and maintenance of production-ready ML-powered software.
1.    Develop ML-powered solutions’ application components:
1.1. Data pipeline
1.2. Code optimization, incl. all relevant integration points
1.3. Packaging
1.4. Installation scripts
1.5. Configuration items
2.    Map and evaluate MLOps gaps in the solution, develop solutions when required (CT/CM)
2.1. Model monitoring (data/model drift)
2.2. Model scheduling
2.3. Model versioning
2.4. Solution scaling
2.5. Code refactoring
3.    Support ML-powered solutions implementations
3.1. Model tuning, modifications and enhancements following user feedback.
3.2. Quantitive value realization
3.3. Collect requirements from the field.
4.    GenAI knowledge and practical experience
5.    Lead solution architecture activities and finalize with designated solution architect.
6.    When applicable, develop pre-production evaluation services.
7.    Lead application testing (UT, ST, FT, NFT) efforts.
8.    Design and support the solution’s CI/CD pipeline, including embedded automated tests.
9.    Extend and introduce new ML/ML Ops tools, libraries, and frameworks.
10.  Research and implement ML algorithms and tools.

All you need is...

• Relevant Experience Required: 2-6 years
• Proficient in Python programming language, ability to write robust code.
• Solid software engineering skills – familiarity with the Agile methodology, modern approaches (containerization and container orchestration), CI/CD pipelines.
• Familiarity with the Python Data Science stack – e.g., scikit-learn, Pandas, Tensorflow / Keras / PyTorch
• Hands on experience working on Unix/Linux environments.
• Hands on experience with building E2E ML pipeline – Business understanding, EDA, data preparation, modeling, and evaluation
• Experience with Azure – significant advantage
• Deep knowledge of math, probability, statistics, and algorithms – advantage
• Experience with Large Language/multi-modal models - advantage
• Outstanding analytical and problem-solving skills
• Team player
• Customer facing
• Autodidact