Top Tech Jobs & Startup Jobs

Owns data architecture and scalable engineering systems for an AI-native finance platform. Responsibilities include canonical data modeling, configurable connectors, SQL/dbt transformation, warehouse optimization, orchestration, CDC, lineage, data quality, schema evolution, and audit-ready traceability. The role also supports curated data for ML and LLM systems, customer-hosted multi-cloud deployments, mentoring distributed engineers, and collaboration with AI, backend, product, and US-based leadership.

Owns application and API services for a multi-tenant, multi-cloud AI platform serving enterprise finance teams. Responsibilities include designing versioned APIs, building review and approval workflows, enforcing authentication, authorization, and tenant isolation, creating auditable systems, supporting customer-hosted deployments, and managing service performance, reliability, observability, and cost. The role also establishes backend patterns, reviews designs, mentors engineers, and partners with data, AI, and product teams.

Owns core backend computation services that produce correct, reproducible, traceable, and auditable financial results. Designs configuration-driven architectures, data-intensive services, and set-based warehouse computations. Requires strong Python, SQL, data modeling, system design, reliability, isolation, and testing expertise. Provides technical leadership through design reviews, engineering standards, and mentorship while partnering with data, AI, and product teams.

Own and build multi-tenant backend APIs, authentication and authorization, review and approval workflows, audit trails, and tenant-isolated services. Design versioned contracts, ensure correctness and traceability, support multi-cloud and customer-hosted deployments, and manage performance, reliability, observability, and cost. Establish backend patterns, review designs, mentor engineers, and collaborate with data, AI, and product teams. Requires strong practical security, API design, and production service ownership.

Build and own production-grade data pipelines, source connectors, warehouse transformations, curated models, reconciliation, lineage, and data quality systems. Design scalable dbt projects and orchestration workflows using Airflow or Dagster, optimize warehouse performance, integrate messy enterprise systems, and ensure pipelines are reliable, idempotent, auditable, and cost-effective. Collaborate with backend and AI teams, establish data contracts, review work, and mentor engineers.

Own the AI and intelligence layer for a finance platform, including customer-specific ML models, anomaly detection, LLM and agentic systems, RAG, grounding, evaluation, and hallucination controls. Build production-grade Python services, ensure leakage-free modeling and faithful outputs, deploy across customer cloud environments, and provide technical leadership while mentoring engineers and partnering with product, data, backend, and evaluation teams.

Lead QA automation strategy and people management while building comprehensive API, UI, backend, performance, security, and database test frameworks. Develop Python and TypeScript automation, integrate quality gates into CI/CD, validate multi-tenant SaaS isolation, and support compliance requirements. Collaborate with Product, Engineering, and DevOps on testability, releases, metrics, and risk communication. Preferred experience includes AI/LLM testing, fintech, observability, chaos engineering, and startup environments.

Lead QA automation strategy and people management while building comprehensive API, UI, backend, performance, security, and database test frameworks. Integrate quality gates into CI/CD across cloud and multi-tenant SaaS environments, validate compliance and data isolation, and communicate quality risks to technical and business stakeholders. Preferred experience includes AI/LLM testing, financial services, observability, chaos engineering, and startup environments.

Build production AI systems for finance teams, including per-customer anomaly detection models, grounded natural-language generation, agentic workflows, and managed LLM integrations. Develop Python services with SQL and warehouse access in containerized customer deployments. Establish rigorous evaluation, anti-leakage, hallucination, grounding, and faithfulness controls, incorporate human confirmation checkpoints, and convert user feedback into durable model and system improvements.

Build and lead quality engineering for a multi-tenant AI platform. Responsibilities include creating API, UI, backend, performance, security, and database automation; integrating tests into CI/CD; validating cloud and Kubernetes deployments; ensuring tenant isolation and compliance evidence; defining QA standards and release processes; mentoring engineers; and communicating quality risks. Preferred experience includes testing AI/LLM applications, financial systems, observability, chaos engineering, and startup environments.