Staff AI 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 **Staff AI Engineer** to own the AI and intelligence layer — and 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 own the machine-learning models that learn each customer's patterns, the LLM and agentic systems that produce grounded natural-language output, and the rigor that keeps that output faithful. You build per-customer models without leaking the very signal they're meant to detect, and you set a high bar for evaluation and grounding across the layer.

This is a senior, hands-on role. You set technical direction for the AI layer, partner closely with the evaluation engineer to make quality measurable, and turn user feedback into durable improvements.

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

Staff-level applied ML/AI experience in production. You have shipped and owned real ML and AI systems used by customers, and you have set technical direction for others, not only built prototypes.

Strong classical machine learning. Hands-on depth in time-series modeling, anomaly and outlier detection, and gradient-boosted models such as LightGBM or XGBoost, using Python, scikit-learn and pandas or Polars.

Modern LLM and agent patterns. You have built retrieval-augmented generation (RAG), tool-using agents, and structured (schema-validated) output, and orchestrated them with LangGraph or a similar framework.

Rigor on evaluation, grounding and hallucination control. You know how to measure whether an LLM's output is faithful to its source data, and you have built guardrails that stop unsupported statements from reaching users.

Discipline around data leakage and honest evaluation. You design features, splits and validation so models are never trained or scored on information they would not have in real use.

Clear thinking about what ML can and cannot claim. You can explain to non-ML colleagues where a model's output is evidence and where it is only a signal, and why that line matters when results sit next to reported financials.

Real software engineering. You write tested, production-grade Python services, are comfortable with SQL against warehouses like Snowflake or PostgreSQL, and have shipped containerized software.

Technical leadership. You mentor engineers and work closely with backend, data, product and evaluation colleagues to turn feedback into lasting improvements.

Nice-to-haves

Finance or accounting domain experience. Familiarity with ERP, billing or FP&A data and the audit expectations around it.

Feature-attribution literacy. Experience with tools like SHAP, and a clear view of where attribution explanations break down.

Human-in-the-loop systems. You have designed review, confirmation or correction workflows and fed them back into models.

Managed LLMs inside customer clouds. Experience running AWS Bedrock, Google Vertex AI or Azure OpenAI inside a customer's VPC so their data never leaves their environment.

Sparse and zero-inflated data. Experience modeling rare events or data that is mostly zeros.

Multi-deployment enterprise software. Exposure to products shipped as multi-tenant SaaS, single-tenant SaaS and on-premises across AWS, Azure and GCP.