Senior Staff Data Engineer

About Meeru AI Meeru AI is building an AI-native platform that transforms how finance and accounting teams operate. We connect to enterprise financial systems — ERPs, CRMs, billing platforms, HRIS — and apply machine learning to turn fragmented operational data into grounded, auditable intelligence for CFOs, controllers, and FP&A leaders. We deploy on customer terms — SaaS multi-tenant, SaaS single-tenant, and on-premises — across AWS, Azure, and GCP . Our customers are Fortune 500 finance teams who require data isolation, auditability, and compliance.

The Role We are looking for a Senior AI Engineer to build the AI and intelligence layer — and help uphold the discipline that keeps it honest. Our output sits adjacent to externally reported financials, so "sounds plausible" is not good enough: everything the AI produces must be grounded in, and traceable to, verified data. You'll build the machine-learning models that learn each customer's patterns, the LLM and agentic systems that produce grounded natural-language output, and help uphold the rigor that keeps that output faithful. You own significant pieces of the layer end-to-end, working closely with our Staff AI Engineer and evaluation engineer, and you build per-customer models without leaking the very signal they're meant to detect. This is a hands-on engineering role. You turn designs into robust production systems, measure quality rigorously, help turn user feedback into durable improvements, and grow toward staff-level technical ownership. Location & engagement Offshore — Open to remote globally, working with our distributed engineering team. Flexible engagement — open to full-time, contract, or contract-to-hire , whichever fits you and the engagement best. US partnership — you partner closely with US-based AI leadership, with a few hours of daily time-zone overlap. What makes this role different Grounding is a hard requirement — no fabricated or unsupported output, ever. Faithfulness is a gate, not a tuning goal. Clear separation of concerns — ML surfaces and prioritizes; generated text only states what is supported by verified data or confirmed by a human. Per-customer intelligence — models that learn each business's behavior, with strict anti-leakage discipline. Deploys in customer clouds — including managed LLMs in-VPC, so customer data never leaves their environment.