Requirements:

Bachelor’s degree or equivalent practical experience.
5 years of experience in coding, in one or more of the following languages (C++, Java, Go or Python).
3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
3 years of experience with developing large-scale infrastructure, distributed systems or networks, or experience with compute technologies, storage or hardware architecture.
Experience in the financial services industry, particularly in quantitative finance, algorithmic trading, or risk management.

Preferred qualifications:

Master's degree or PhD in Computer Science or related technical field.
5 years of experience in data structures and algorithms.
1 year of experience in a technical leadership role.
Experience with machine learning, natural language processing (NLP), or generative AI and large language models (LLMs).
Experience building complex data processing pipelines with tools like Kafka, Apache Beam (Dataflow) or Spark and with time-series data and financial market data feeds.
Understanding of Google Cloud Platform services (e.g., BigQuery, Google Kubernetes Engine, Pub/Sub).

Design, develop, and deploy core components of the financial AI platform, including the orchestration engine, proprietary analytical tools, and data processing pipelines.
Build robust, high-performance microservices for complex financial workflows, such as historical backtesting, Monte Carlo simulations, and risk calculations.
Collaborate with Google's research teams to integrate and optimize state-of-the-art forecasting and time-series analysis models for financial use cases.
Develop an intelligent orchestration layer using Google's latest large-scale models to interpret natural language queries and chain together the appropriate sequence of financial tools and data sources.
Engineer scalable and auditable data pipelines for ingesting and transform various data sets (including real-time feeds and proprietary data), ensure the reproducibility and verifiable logic of all AI-generated insights.