Applied AIML, Senior Associate

As a Senior Applied AI/ML Associate within our dynamic team of innovators and technologists, you will revolutionize how the Bank services and advises clients, deepen client engagements, and promote process transformation.

You will analyze existing processes and vast amounts of data to design autonomous AI agents.

We seek individuals passionate about leveraging advanced data analysis, statistical modeling, and AI/ML techniques to solve complex business challenges through high-quality, cloud-centric software delivery.

Our culture thrives on experimentation, continuous improvement, and learning.

You will work in a collaborative, trusting, and intellectually stimulating environment-one that values diversity of thought and fosters creative solutions that serve the best interests of our global clientele.

Job Responsibilities


* Build and maintain end-to-end batch and streaming data pipelines (ingestion → transformation → curation → serving) on AWS.


* Develop scalable transformations using Spark (PySpark/Scala) and SQL, optimizing for performance, reliability, and cost.

Implement and optimize Snowflake solutions (schemas, tables, views, micro-partitioning/clustering considerations, warehouse sizing, query tuning, data sharing patterns where applicable).


* Understanding of data models for enterprise analytics (dimensional/star schemas, normalized models; applying standard banking data concepts where relevant).

Create robust orchestration and scheduling (e.g., Airflow/MWAA, AWS Step Functions, etc.) with monitoring, alerting, retries, and operational runbooks.


* Ensure data quality through validation checks, reconciliation, and controls (e.g., completeness, timeliness, accuracy, deduplication).

Implement security and governance controls suitable for banking: encryption, least-privilege access, auditability, data retention, and lineage/documentation.


* Collaborate with stakeholders across technology, risk, compliance, analytics, and product to translate requirements into reusable, well-defined datasets and support production operations: incident triage, root-cause analysis, performance tuning, and continuous improvement of pipeline stability.


* Utilize AI-powered tools and large language models (LLMs) to accelerate data pipeline development, automate code generation, and streamline debugging and documentation processes.

Apply AI-driven techniques for data quality validation, anomaly detection, and automated data profiling to ensure the integrity and reliability of data assets.


* Collaborate with data science and ML teams to design, build, and maintain scalable data infrastructure that supports the training, deployment, and monitoring of AI/ML models.

Continuously evaluate and adopt emerging AI technologies to optimize ETL workflows, reduce manual effort, and enhance overall engineering productivity.

Required qualifications, capabilities, and skills


* Advanced degree (MS or PhD) in a quantitative or technica...