Staff Software Engineer - AI

Your Daily Adventures Will Include:

Leading the architecture, design, and delivery of distributed cloud-native applications capable of high concurrency and demanding real-time data needs.
Designing and building production-grade data pipelines and ETL/ELT workflows — modeling data for both transactional (OLTP) and analytical (OLAP) use, and orchestrating them with modern workflow tooling.  
Integrating ML and GenAI capabilities into product features — from model serving and evaluation to LLM-powered enrichment, retrieval (RAG), and intelligent automation within our services. 
Championing data quality and correctness — building validation, observability, and testing into every step of the pipeline so downstream analytics and AI features can be trusted. 
Collaborating with data science, product, and engineering partners to ship intelligent, complex product features.
Setting and promoting engineering standards for code quality, security, and operational excellence; nurturing automation and continuous improvement.
Diagnosing and eliminating performance bottlenecks and proactively addressing reliability risks.
Mentoring, reviewing code/architectures, and fostering a culture of rapid learning.
Decomposing legacy systems into SOA/microservices, resolving tech debt, and evolving the architecture for scale.
Taking end-to-end ownership of major initiatives from planning through impact.
 
What Sets This Role Apart: Data & AI Engineering
This role blends strong backend engineering with hands-on data and AI capability. You will be expected to grow the team’s fluency in these areas, so we look for depth (or a clear trajectory) across:
Data modeling & storage: schema design, query optimization, and knowing when to reach for RDBMS, NoSQL, OLAP, or OLTP stores.
Modern data stack: Spark / Delta Lake, Databricks or Snowflake, dbt for transformation, and Airflow (or similar) for orchestration.
AI/ML in production: deploying and monitoring models, feature pipelines, and — increasingly — GenAI/LLM application patterns (embeddings, vector search, RAG, prompt/context engineering, evaluation).
Analytics enablement: building the data models, frameworks, and artifacts that make trustworthy analytics and dashboards easy for others to build on.

Our Vision of You:

Required Qualifications
• Demonstrated excellence designing and operating large-scale distributed systems with cloud service-oriented architecture.
• Proven leadership in fast-paced environments, setting standards, and inspiring technical teams to exceed delivery goals.
• Mastery in backend programming (Go required; Python strongly valued for data/AI work; Java/Ruby a plus) and hands-on with distributed data platforms (Kafka, RabbitMQ, NoSQL).
• Hands-on data engineering: building and operating data pipelines/ETL, data modeling and schema design, and a rigorous focus on data quality and correctness. 
• Experience building APIs and analytics/data infrastructure, and deploying ML algorithms in production.
• Excellent communication and cross-team collaboration skills.
• Commitment to security, compliance, and robust, scalable design.
• Growth mindset — always learning, always elevating the technical bar for the team.
 
Strong Pluses
Not required on day one, but a meaningful differentiator — and where we most want this hire to help the team grow.
Experience with the modern data stack: Spark/Delta Lake, Databricks or Snowflake, dbt, and Airflow.
Hands-on GenAI/LLM application experience: RAG, vector databases, embeddings, prompt/context engineering, and model/output evaluation.
MLOps exposure: feature stores, model serving, and monitoring in production.
Experience shaping data as a product — dashboards, semantic/metrics layers, and analytics enablement for other teams.