Senior AI Engineer

MeeruAI; Hyderabad; On-site; INR 900,000 - 1,500,000; Full time

Job summary

Meeru AI is seeking a Senior AI Engineer to own and build the AI and intelligence layer for a finance-focused platform. You will develop per-customer ML models for pattern learning, design grounded NLP layers, and implement agentic orchestration with strict verification.

You will work closely with a Staff AI Engineer and an evaluation engineer to ensure data-grounded, auditable outputs. You will deploy production Python services that access Snowflake and PostgreSQL, integrate LLMs in customer

Qualifications

• Experience building per-customer ML models using time-series and operational data with baseline statistics.
• Strong Python ML stack knowledge and model evaluation discipline.
• Production-grade ML services deployment and monitoring.
• Ability to design guarded, data-grounded NLP outputs and guardrails.

Responsibilities

• Build per-customer ML models that learn normal vs. anomalous behavior.
• Develop template-first NLG layers grounded in verified data.
• Implement agentic orchestration with verification checkpoints.
• Integrate managed LLMs in customer clouds (AWS Bedrock, GCP Vertex AI, Azure OpenAI).
• Collaborate with evaluation engineer to gate grounding and faithfulness in CI.
• Iterate on user feedback to improve models and systems.
• Ship production Python services with SQL/warehouse access in containerized deployments.

Skills

Time-series modelling

Anomaly detection

LightGBM/XGBoost

Grounded NLP

Guardrails

LangGraph

LLMs in customer clouds

CI validation

Python production services

SQL/warehouse access

Tools

Snowflake

PostgreSQL

Docker

Kubernetes

LightGBM

XGBoost

LangGraph

AWS Bedrock

GCP 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.

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

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