Requirements:

Bachelor’s degree or equivalent practical experience.
8 years of experience in coding in one or more of the following languages (C++, Java, Go or Python).
5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture.
5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
Experience in the financial services industry, particularly in quantitative finance, algorithmic trading, or risk management.

Nice to Haves:

Master's, or PhD in Physics, Mathematics, Computer Science or a related quantitative field.
8 years of experience in data structures and algorithms.
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.
Experience working 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 advanced risk calculations.
Collaborate with researchers from teams like DeepMind to integrate and optimize Google's state-of-the-art forecasting models for financial use cases.
Develop the intelligent orchestration layer that uses Gemini 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.