Principal Software Engineer, KYC Risk Assessment

What you'll do

• Architect and implement complex, scalable engineering frameworks using modern software design principles.
• Develop secure, high-quality production code for data-intensive applications and mentor other engineers.
• Create durable, reusable software patterns that are utilized across multiple teams and functions.
• Design and govern agentic AI systems including multi-agent workflows and human-in-the-loop controls.
• Establish engineering standards for LLM-based applications such as RAG pipelines and vector store integrations.
• Architect and govern AI-enabled engineering workflows to improve delivery speed, code quality, and operational outcomes.
• Serve as a subject matter expert in data engineering, platform architecture, or AI systems.
• Influence senior stakeholders on technical strategy and the adoption of advanced industry standards.

What we're looking for

• Formal training or certification on software engineering concepts and 7+ years of applied experience.
• Hands-on experience delivering system design, application development, testing, and operational stability at enterprise scale.
• Experience designing and deploying production AI/ML systems, including LLM-based applications and agentic architectures in regulated environments.
• Expertise in one or more programming languages, particularly Python and/or Java.
• Advanced knowledge of software application development in disciplines such as cloud, AI/ML, or data engineering.
• Experience with large-scale data processing, microservices, API design, Kafka, Redis, MemCached, observability tools, and orchestration frameworks.
• Advanced working knowledge of relational and NoSQL databases, vector stores, data lake architectures, and data governance.
• Practical cloud-native experience (AWS, Azure, or GCP).
• Experience with modern data platforms such as Databricks or Snowflake (preferred).
• Deep hands-on experience with Spark/PySpark and other big data processing technologies (preferred).
• Expertise in open-source table formats and catalog services such as Apache Iceberg (preferred).
• Experience with LLM orchestration frameworks, model serving infrastructure, or managed endpoints (preferred).
• Familiarity with AI evaluation and observability practices including evals, red-teaming, and monitoring for LLM workloads (preferred).
• Understanding of agentic design patterns and awareness of AI risk/regulatory considerations in financial workflows (preferred).