Meeru AI Inc

Build customer-specific machine-learning models and agentic systems grounded in verified financial data, with rigorous evaluation and verification checkpoints. | Requires production-grade Python services and machine-learning models for time-series and operational data, plus LLM orchestration and strong data integrity, auditability, and compliance practices. | 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.

Build and maintain an AI intelligence layer grounded in verified financial data while owning data architecture and collaborating with AI and evaluation engineers. | Requires 10+ years specifically in data engineering, including data modeling, SQL, production orchestration, data correctness, and mentoring distributed teams. | 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. 10+ years in data engineering, including significant time at Staff, Principal or Lead level. You have owned data architecture for a whole system, not just individual pipelines. You have designed a canonical or common data model used by many teams. You know how to define shared entities, hierarchies, dimensions and time periods so that many different sources fit into one model, and many consumers can rely on it. You build so that new customers or sources are added by configuration, not by rewriting code. You have designed mapping frameworks or connector strategies that reuse work rather than fork it, and you can explain when to build versus configure. Expert SQL and dbt (or similar) modeling at scale. Window functions, large joins, incremental and change-data-capture (CDC) models, and performance tuning on large datasets are everyday tools for you. Deep warehouse knowledge: Snowflake, BigQuery and/or PostgreSQL. You understand partitioning, internals, and the cost-versus-performance trade-offs of each. Production orchestration with Airflow, Dagster or Prefect. Your pipelines are idempotent and reproducible: the same inputs always produce the same outputs, and re-runs are safe. Lineage, data contracts, data quality and schema evolution are second nature. You have set up lineage capture (e.g. OpenLineage, dbt exposures), quality tests (dbt tests, Great Expectations), and checks that stop a build when numbers don't reconcile. A firm commitment to correctness and traceability. "Every number ties back to its source" is a standard you hold, even under deadline pressure. Our data sits next to companies' reported financials and must stand up to audit. Strong Python for pipelines, tooling and testing. Experience leading and mentoring data engineers, ideally across distributed or offshore teams. You run design reviews, write standards, and raise the bar without becoming the bottleneck. Clear communication with architects, backend and AI engineers, and product. You can explain and defend architecture decisions in writing and in meetings, in English, with US-based colleagues. Willingness to work part of your day overlapping with US hours. Our leadership is US-based. Nice-to-haves Financial and ERP source data. Hands-on experience with NetSuite, SAP, Oracle or Workday data, and an understanding of how financial statements are built and reconciled (general ledger, AP, payroll, accruals, prepaids, equity). Multi-cloud and customer-hosted deployments. You have run data workloads across AWS, Azure and GCP, including inside a customer's own cloud with Docker/containers, strict tenant isolation and no data leaving their environment. FinTech or financial-services background, including exposure to SOC 2 or audit expectations for data. Synthetic or "golden" test datasets. You have built realistic test data so teams could develop and regression-test before real customer data was available. Feeding ML or LLM systems from a curated data layer. You understand what AI teams need from clean, well-documented data.

Lead the backend team and design reliable, configuration-driven data services for traceable and auditable financial intelligence. | Requires 8+ years of backend engineering, Staff+ experience, proficiency in Python, Go, or Java, and strong SQL, data modeling, and reliability expertise. | 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 Staff Backend Engineer to own the core services at the heart of the platform — the data-intensive computation layer that turns curated financial and operational data into correct, reproducible, fully traceable results. This is the structured engineering beneath the product: not dashboards, not an LLM wrapper, but the services that do the heavy computation and stand behind every number the platform produces. You build reusable, configuration-driven computation components, enforce strong controls and guardrails over automated output, and guarantee that every result reconciles and can be reproduced and audited. You provide technical leadership for the backend team and partner with data, AI, and product to translate domain logic into robust, reusable services. What makes this role different The output of this layer sits next to externally reported financials, so correctness is not negotiable. Everything you build is held to a hard standard: every result must be traceable to source, reproducible, and auditable, with clear guardrails that keep verifiable results separate from anything that requires human review — and nothing is ever quietly fudged to make the math look complete. 8+ years of backend engineering in Python, Go, Java, or similar, including significant time at Staff+ level owning complex, data-intensive systems. Production Python with strong service design and testing practices. Elite SQL and data modeling. You’re fluent in window functions and large-scale joins, and you reason rigorously about both correctness and performance. Set-based, reproducible computation over a data warehouse such as Snowflake or PostgreSQL. Configuration-driven architecture. You’ve designed extensible services or rules engines where new use cases ship as configuration, not new code paths. Strong system design for reliability, isolation, and reproducibility. A correctness mindset. You treat “every result is traceable, explainable, and reproducible” as a hard requirement. Technical leadership. You set patterns, review designs, and mentor engineers. Nice-to-Haves Finance, accounting, or analytics domain experience, or genuine curiosity about how financial statements are built and reconciled. dbt alongside Snowflake or PostgreSQL. Multi-tenant or customer-hosted (on-prem) deployments, ideally across AWS, Azure, and GCP. Docker, Kubernetes, and Terraform. Audit logging, data lineage, and feature-flag or controls systems. Auditable systems in regulated environments such as fintech, banking, or healthcare. Exposure to ML, statistical workflows, or LLM application patterns, enough to integrate them well.

Own and operate multi-tenant backend application and API services ensuring performance, security, and deployment readiness while mentoring engineers. | Seven+ years backend engineering with staff-level ownership, strong Python framework skills, multi-tenant SaaS experience, and proficiency in PostgreSQL, Redis, Docker, Kubernetes, and Terraform. | Engineering, Platform Pleasanton, CA (Hybrid) (USA) Full-time 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, including ERPs, CRMs, billing platforms, and 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 The Staff Backend Engineer owns the application and API services layer: the services through which everything the platform computes actually reaches a person. Multi-tenant APIs, authentication and authorization, and the review and approval workflows where a controller accepts a result, challenges it, or sends it back. The computation layer produces the numbers. This layer is where a finance team sees them, interrogates them, approves them, and acts on them. That means your services carry the same correctness and auditability requirements as the computation beneath them, plus the access control and tenancy guarantees that let a Fortune 500 finance organization trust the platform with their close cycle. Every action taken through your services is part of an audit trail that sits next to externally reported financials. Who approved what, when, on the basis of which underlying result, and what changed afterward. That is not logging you add later. It is a product requirement, and it shapes how the services are designed from the first commit. This is hands-on and high-ownership. You own major services end to end, set backend patterns alongside the Senior Staff engineers, and partner with data, AI, and product on the contracts between layers. Key Responsibilities 1. Own the multi-tenant APIs through which the platform's results are consumed. Design the service contracts between the application layer and the computation, data, and AI layers, and own the versioning discipline that lets those contracts evolve without breaking consumers who cannot all move at once. 2. Build the review, approval, and exception-handling workflows finance teams operate in. This is where the product either earns trust or loses it. A controller who cannot see why a number is what it is will not approve it, and will not use the platform twice. 3. Build and maintain multi-tenant isolation at the service layer. Own authentication, authorization, and role-based access across the application surface. Cross-tenant leakage should be impossible by construction rather than prevented by convention. 4. Build the audit trail. Who saw what, who approved what, on the basis of which result. Guarantee that every user-facing figure links back to its computation and its source, and support the enterprise security and compliance reviews Fortune 500 finance customers run before they buy. 5. Build for a multi-cloud-ready, multi-tenant-ready, customer-hosted deployment model. Services that run inside a customer's environment with strong isolation and no data egress. 6. Own performance, reliability, and cost of the services you build, including observability and operational health. You should be able to answer why a request is slow rather than guessing. 7. Set backend patterns, review designs, and mentor mid and senior engineers. Partner with data, AI, and product on cross-layer service design. A note on accountability: this role owns the application and API services, tenancy and access control, and the audit trail. It does not own the core computation layer, the data foundation, the AI layer, or product prioritization. Seven or more years in backend forward deployment engineering, with significant Staff-level time owning complex production services. Owning them, including what happens when they fail. Demonstrated ownership of services in a multi-cloud SaaS environment, and a clear account of how you guaranteed isolation rather than assumed it. An obsession with correctness and traceability. You treat "who did what, when, and on what basis" as a first-class requirement rather than an audit checkbox. Strong API and service design, including versioning, contracts, and backward compatibility. You have evolved an interface other teams depended on without breaking them. Practical security engineering: authentication, authorization, and tenant isolation. Not theory. Python FastAPI, Django, or Flask; PostgreSQL and Redis; REST API design; Docker, Kubernetes, and Terraform SQL against Snowflake or PostgreSQL. Nice to Have Finance, accounting, or fintech domain, or a genuine willingness to learn how financial statements are built and reconciled. Multi-tenant, customer-hosted deployment at scale. Supporting SOC 2 or ISO 27001 review. Building auditable systems in a regulated environment. Exposure to ML or LLM application patterns, enough to integrate them well. Health and wellbeing. Medical, dental, and vision insurance, with MeeruAI covering 50 % of employee premiums Flexible spending accounts, health savings accounts with a company contribution Unlimited PTO and 12 paid holidays. 401(k) with company matching