Senior Software Engineer, Financial AI
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Minimum qualifications:
- 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).
About the job
Google Cloud's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google Cloud's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. You will anticipate our customer needs and be empowered to act like an owner, take action and innovate. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
In this role, you will build a new category of AI-powered tools for financial markets. You will create a transformative platform that functions as an intelligent co-pilot for investors, analysts, and portfolio managers, enabling them to move from complex questions to informed action in minutes.
By combining Google's cutting-edge large-scale models, advanced AI research, and unique data insights, you will build a system that can reason over the intricate interplay of market data and world events. You will be at the forefront of applied AI, building a product that will empower financial institutions to innovate and establish Google Cloud as a strategic partner in their success.
Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
Responsibilities
- 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.
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