Senior Machine Learning Engineer

Arbisoft is seeking a highly skilled and experienced Senior Machine Learning Engineer to join our dynamic team. The ideal candidate will have a robust background in Python machine learning, time series analysis, financial market data, DevOps, and cloud services. This role is pivotal in leveraging advanced data science techniques to extract valuable insights from complex datasets, contributing to Arbisoft's commitment to innovation and excellence.

Key Responsibilities:

• Machine Learning and Data Science:
• 5+ years of proficiency in Python and machine learning libraries (pandas, NumPy, statsmodels, sklearn, tensorflow).
• Extensive experience in time series analysis, including ARIMA, time series decomposition, multivariate time series analysis, and outlier detection.
• Knowledge of advanced techniques such as SVD/PCA, LSTM, NeuralProphet.
• Familiarity with ensemble methods such as Xgboost/LightGBM.
• Expertise in Spark, PySpark for big data processing.
• Financial Market Data:
• 5+ years of hands-on experience with financial market data and financial data analytics.
• DevOps and Cloud Services:
• 5+ years of DevOps experience, including cloud services from Amazon AWS.
• Proficiency in dockerized deployment, data engineering DevOps, and container orchestration using Docker and Kubernetes.
• Experience with microservices, Flask, FastAPI, and deployment on platforms like Sagemaker.
• Technical Proficiency:
• Strong proficiency in MS Excel for spreadsheet analysis.
• Solid understanding of object-oriented programming (OOP) principles and software architecture.
• Experience with code design patterns, logging patterns, and RESTful APIs in Python.
• GIT version control proficiency.
• Database Skills:
• Strong SQL knowledge and relational database skills (e.g., PostgreSQL, Snowflake).
• Additional Skills:
• Familiarity with CUDA, Triton, TensorRT, ONNX for machine learning optimizations.
• Experience with Elastic Caches such as Redis and MemCache.
• Proficiency in PyTorch and CUDA GPU optimizations.
• Knowledge of Bayesian Hyperparameter Optimization (HPOs).
• Familiarity with AWS Step Functions, AWS Batch for orchestration and job scheduling.

Preferred Qualifications:

• Master’s or Ph.D., or equivalent degree in data engineering, machine learning, or a related field.
• Experience with PyCharm IDE.

Additional Information:

• The candidate should demonstrate a deep understanding of financial data analytics and have strong communication and teamwork skills.
• The role involves working on both online and offline platforms, hence a versatile skill set is desirable.