Senior AI Engineer

Meeru AI Inc; Canada; On-site; CAD 120,000 - 180,000; Full time

Job summary

Meeru AI Inc is building an AI-native platform to transform how finance and accounting teams operate. We are seeking a Senior AI Engineer to build the AI layer and ensure outputs are grounded in verified data.

You will develop per-customer ML models, design a grounded NL layer, and implement agentic orchestration. You will deploy in customer clouds and ship production Python services connected to Snowflake and PostgreSQL.

Qualifications

• Hands-on AI/ML engineer: build production ML models with strong grounding in verified data.
• Experience delivering time-series anomaly detection and model validation in fintech contexts.
• Proficient in Python, building robust services with data access to warehouses.

Responsibilities

• Build per-customer ML models learning normal vs. anomalous behavior on time-series data with strict anti-leakage discipline.
• Create a template-first natural-language generation layer grounded in verified data with guardrails.
• Develop agentic orchestration with verification and human-confirmation checkpoints.
• Integrate managed LLMs running in customer clouds (AWS Bedrock, GCP Vertex AI, Azure OpenAI).
• Collaborate with evaluation engineers to gate grounding and faithfulness checks in CI.
• Ship production Python services with SQL/warehouse access in containerized, customer-hosted deployments.

Skills

ML modeling

Time-series anomaly detection

Python production services

LangGraph / agent orchestration

LLM integration

Data grounding / faithfulness checks

CI/CD / production readiness

Tools

LightGBM

XGBoost

Snowflake

PostgreSQL

Bedrock

Vertex AI

Azure OpenAI

Job description

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.

• Build per-customer ML models that learn normal vs. anomalous behavior on time-series and operational data, using statistical baselines and gradient‑boosted models (LightGBM/XGBoost) with strict anti‑leakage discipline.
• Build a template‑first natural-language generation layer that is strictly grounded in verified data, using guardrails and structured output to prevent hallucinated or unsupported statements.
• Implement agentic orchestration (LangGraph or equivalent) with verification and human‑confirmation checkpoints.
• Integrate managed LLMs running inside customer clouds (AWS Bedrock, GCP Vertex AI, Azure OpenAI).
• Partner with an evaluation engineer to gate grounding, hallucination, and faithfulness checks in CI.
• Turn user feedback into durable model and system improvements.
• Ship production Python services with SQL/warehouse access (Snowflake, PostgreSQL) in containerized, customer‑hosted deployments.

Nice‑to‑Have

• Finance or accounting domain experience (fintech, FP&A, ERP, audit, financial close).
• Feature‑attribution experience (e.g. SHAP), along with an understanding of its limits.
• Human‑in‑the‑loop systems and feedback loops.
• Hands‑on work with Bedrock, Vertex AI, or Azure OpenAI in VPC or private deployments.
• Zero‑inflated or sparse‑event modeling.