Senior ML Engineer

Experience: 5 to 10 years of professional experience in machine learning and software engineering, with a track record of shipping production-grade services. (Not more than 10 years) ML Fluency: Hands-on expertise in modern AI architectures (LLMs, GNNs, Deep Learning) as well as classical modeling (Gradient Boosting, Regression). The AI Stack: Deep proficiency with the Generative AI ecosystem, specifically LangGraph, LangSmith, Google ADK, Google Vertex AI and AWS bedrock. Domain Expertise: Specialized knowledge in at least one area: NLP, Information Retrieval, Computer Vision, or Advanced Statistical Modeling. Engineering Rigor: Ability to write clean, maintainable, and highly-tested code (Python) that directly solves customer pain points. Communication: Exceptional written and verbal communication skills; ability to translate complex technical concepts for non-technical stakeholders. Mission-Driven: A genuine passion for building an open, global financial system.

1. Nice to haves: Education:
   Advanced degree (Masters or PhD) in CS, ML, Statistics, or a related quantitative field. Data Infrastructure: Experience with large-scale data tools such as Spark, Kafka/Kinesis, Snowflake, or Databricks. Workflow Orchestration: Familiarity with Apache Airflow or similar DAG-based orchestration tools. Ethical AI: Deep understanding of model interpretability, bias mitigation, and responsible AI governance. Expertise in data analysis and visualization

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