Jr. ML Engineer

Bosch is seeking a motivated and enthusiastic Junior Machine Learning Engineer with a specialization in Time Series analysis to join their advanced engineering team. This role involves working with Corporate Research (CR) to develop high-quality AI solutions. The ideal candidate will possess a foundational understanding of time series concepts, process curve use-cases, and relevant tools and technologies. You will collaborate with senior engineers and researchers to develop and implement Machine Learning algorithms and solutions for various applications involving time series and tabular data.

Key Responsibilities:

• Algorithm Development: Assist in the design and implementation of machine learning algorithms for regression, classification, clustering, and deep learning tasks.
• Data Processing: Contribute to the preprocessing and augmentation of datasets to ensure high-quality input for model training and evaluation.
• Model Training: Support the training and evaluation of ML models, optimizing them for accuracy, performance, and scalability, and assessing performance metrics.
• Software Development: Gain hands-on experience integrating AI solutions into existing applications and systems.
• Model Deployment: Deploy and monitor machine learning models in production environments, implementing CI/CD pipelines for model integration and updates.
• Testing and Debugging: Participate in testing and debugging processes to ensure the robustness and accuracy of algorithms and solutions.
• Documentation: Maintain clear and comprehensive documentation of algorithms, experiments, and code for knowledge sharing and reproducibility.
• Collaboration: Work closely with engineers, researchers, and stakeholders to understand project requirements and deliver effective solutions.

Required Skills and Qualifications:

Technical Skills:

• Strong conceptual foundation in: Linear Algebra, Multivariate Calculus, Probability and Stochastic Processes, Optimization Methods, Feature Engineering.
• Experience with algorithms such as: Gradient Boosted Trees, Explainable Boosted Machines, K-Nearest Neighbors (KNN).
• Hands-on proficiency with: Scikit-learn, tsfresh, xgboost, pandas, lightgbm, NumPy.
• Familiarity with MLOps frameworks like MLflow.
• Understanding of widely used ML techniques and algorithms.
• Proficiency in Python. Knowledge in C++ or Java is an advantage.
• Familiarity with databases (SQL and NoSQL).

Soft Skills:

• Problem-Solving: Strong analytical and problem-solving skills, with the ability to work independently and collaboratively.
• Communication: Good communication skills to clearly convey technical concepts to diverse stakeholders.
• Attention to Detail: High attention to detail and a commitment to delivering high-quality work.

Education:

• BE/MS/M. Tech in Electronics or Computer Science.

Experience:

• 3-4 years of experience in a related field.