Jr. ML Engineer - Time Series

Seeking a motivated and enthusiastic Junior Machine Learning Engineer with Time Series background who can work with CR (Corporate Research) and develop high quality AI solutions for our advanced engineering team. The ideal candidate will have a foundational understanding of time series concepts, process curve use-cases and experience with relevant tools and technologies. As a ML Engineer, you will work alongside senior engineers and researchers to develop and implement ML algorithms and solutions for a variety of applications on time series and tabular data. Roles & Responsibilities:

Algorithm Development: Assist in designing and implementing machine learning algorithms for tasks such as regression, classification, clustering, and deep learning.

Data Processing: Contribute to preprocessing and augmenting image datasets to ensure high-quality input for model training and evaluation.

Model Training: Support the training and evaluation of ml models, including Optimize models for accuracy, performance, and scalability and assessing performance metrics.

Software Development: Hands on experience with the software development to integrate AI solutions into existing applications and systems.

Model Deployment: Deploy and monitor machine learning models in production environments. Implement CI/CD pipelines for model integration and updates

Testing and Debugging: Participate in testing and debugging processes to ensure the robustness and accuracy of ensembled algorithms and solutions.

Documentation: Maintain clear and comprehensive documentation of algorithms, experiments, and code to facilitate knowledge sharing and reproducibility.

Collaboration: Work closely with other engineers, researchers, and stakeholders to understand project requirements and deliver effective solutions.
Required skills: Technical Skills - o Strong conceptual foundation on the following:

Linear Algebra

Multivariate Calculus

Probability and stochastic processes

Optimization methods

Feature Engineeringo Strong experience in the following algorithms:

Gradient Boosted Trees

Explainable Boosted Machines

KNC

KNNo Strong hands-on proficiency in the following:

Scikit Learn

tsfresh

xgboost

pandas

lighgbm

NumPy

MLOps frameworks like mlflow

Familiarity with databases (SQL and NoSQL)o Understanding of widely used ML techniques and machine learning algorithms. o Proficiency in programming languages in python, knowledge in C++, or Java are advantage. o Problem-Solving: Strong analytical and problem-solving skills with the ability to work independently and as part of a team. o Communication: Good communication skills with the ability to convey technical concepts clearly to both technical and non-technical stakeholders. o Attention to Detail: High attention to detail and commitment to delivering high-quality work.