Applied AI ML [Multiple Positions Available]

DESCRIPTION:

Duties: Build and train production-grade Machine Learning on large-scale datasets to solve various business use cases for Global Banking.

Use large scale data processing frameworks for feature engineering and be proficient across various data, both structured and unstructured.

Use Deep Learning models and Generative AI techniques for solving various business use cases including multi-source data fusion, information retrieval, question-answering, forecasting and anomaly detection.

Build ML models across Public and Private clouds including container-based Kubernetes environments.

Develop end-to-end ML pipelines necessary to transform existing applications and business processes into robust Artificial Intelligence systems.

Build both batch and real-time model prediction pipelines with existing application and front-end integrations.

Collaborate to develop large-scale data modeling experiments, evaluating against strong baselines, and extracting key statistical insights and cause and effect relationships.

QUALIFICATIONS:

Minimum education and experience required: Bachelor's degree in Computer Engineering, Computer Science, Information Systems, Data Science, Artificial Intelligence, Machine Learning, or related field of study plus 3 years (36 months) of experience in the job offered or as Applied AI ML Associate, Software Engineer, Software Engineering Program Analyst, or related occupation.

The employer will alternatively accept a Master's degree in Computer Engineering, Computer Science, Information Systems, Data Science, Artificial Intelligence, Machine Learning, or related field of study plus 1 year (12 months) of experience in the job offered or as Applied AI ML Associate, Software Engineer, Software Engineering Program Analyst, or related occupation.

Skills Required: This position requires experience with the following: Managing and transforming structured and unstructured data using Pythonic implementations of normalization, aggregation, data cleaning, enrichment, entity extraction, or sentiment analysis; Validating data quality and completeness; Implementing anomaly detection methods and building robust, reusable features for AI/ML teams; Conducting data analysis, modeling, and engineering tasks required in end-to-end AI/ML solution design and implementation using Python, SQL, and PySpark; Exploring, visualizing, and analyzing data to create and validate proof-of-concept AI/ML engineering solutions; Applying machine learning and statistical techniques including regression, classification, clustering, time series analysis, dimensionality reduction, and mathematical optimization to address business challenges; Developing, evaluating, and training AI/ML models, taking ownership of the full implementation lifecycle including hyperparameter tuning, model selection, and performance evaluation; Writing optimized code in SQL, Spark, and Python leveraging cloud technologies and cloud analytics platforms; Designing and deploying scal...