ME
Senior AI Engineer
meeruai · Hyderabad · onsite · Posted 3d ago
Not disclosedFresherfulltimeSourced from
About a minute. You'll make an account on the way through.
About this role
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.
Requirements
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.
Requirements
- 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
- 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
Sourced from foundit
Skills
managed LLMsPostgresqlGCP Vertex Aiaws bedrockXgboostAzure OpenAILightGBMNatural Language GenerationPythonSQLSnowflakeLanggraphMachine Learning