Experience :

- 10-12 years of professional experience in machine learning and software engineering, with a track record of shipping production-grade services.

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.

Nice to haves :

1. Education: Advanced degree (Master's or PhD) in CS, ML, Statistics, or a related quantitative field.

2. Data Infrastructure: Experience with large-scale data tools such as Spark, Kafka/Kinesis, Snowflake, or Databricks.

3. Workflow Orchestration: Familiarity with Apache Airflow or similar DAG-based orchestration tools.

4. Ethical AI: Deep understanding of model interpretability, bias mitigation, and responsible AI governance.