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Build production AI systems for finance teams, including customer-specific anomaly-detection models, grounded natural-language generation, agentic workflows, and managed LLM integrations. Develop Python services with warehouse access and customer-hosted deployments. Implement verification, human-confirmation checkpoints, anti-leakage controls, and CI evaluation for grounding and hallucination. Use customer feedback to improve models and systems while collaborating with AI and evaluation engineers.

Own core backend services for a finance AI platform, building reliable, configuration-driven computation systems over financial and operational data. Ensure results are traceable, reproducible, auditable, and correctly reconciled. Provide technical leadership through architecture, design reviews, engineering standards, and mentorship while partnering with data, AI, and product teams. The role involves production Python, advanced SQL and data modeling, multi-tenant or customer-hosted deployments, and high standards for correctness and system reliability.

Build and own scalable data architecture, canonical data models, configurable source integrations, and reliable pipelines for AI-driven financial intelligence. Use SQL, Python, dbt, cloud data warehouses, and orchestration tools to deliver idempotent, auditable data workflows. Establish lineage, data contracts, quality controls, and schema evolution practices. Lead architecture reviews, mentor distributed data engineers, and support customer-hosted multi-cloud deployments with strict tenant isolation and traceability.

Design and build deterministic, auditable AI engines and conversational agents for finance platforms. Lead architecture across RAG, agent orchestration, structured outputs, grounding, evaluation, verification, and human-in-the-loop workflows. Develop production Python services with SQL and vector retrieval, support multi-cloud and on-premises deployments, mentor engineers, conduct design reviews, and communicate architecture decisions to technical and business stakeholders.

Build and own production-grade data pipelines, source connectors, warehouse transformations, curated data models, reconciliation, and lineage for an AI-native finance platform. Develop dbt projects, orchestrate reliable and rerunnable workflows with Airflow or Dagster, tune warehouse performance, integrate messy enterprise systems, and ensure data correctness, auditability, and tenant isolation. Collaborate with backend and AI teams, establish data contracts, review work, and mentor engineers.

Own the end-to-end architecture of Meeru AI’s AI-native, multi-tenant, multi-cloud platform. Design secure, portable systems spanning customer data foundations, backend services, AI capabilities, and applications. Establish standards for tenant isolation, auditability, traceability, correctness, and configuration-driven onboarding. Provide hands-on technical leadership through reference implementations, design reviews, architecture decisions, and mentoring while partnering with product leaders, founders, customers, and security stakeholders.