Senior AI Engineer – AI Vertical

Team: Engineering – AI Vertical
Experience: 5+ years, including 2+ years building and shipping LLM systems in production
Location: Mumbai – Andheri East
Reports to: Head of Engineering
Employment Type: Full-time

About CashFlo

CashFlo is building the next generation of AI-powered Finance & Accounting solutions for businesses.

India sees over $11 trillion in B2B money flows every year, yet businesses typically wait around 70 days to get paid. CashFlo is transforming this ecosystem through technology across Payments, Lending, SaaS and AI.

Our platform processes ₹20,000+ Cr of invoices every month, serving 300,000+ MSMEs and 1,200+ corporates, with 30+ lenders integrated for credit.

CashFlo is now building a dedicated AI vertical for the global market, and we're looking for a Senior AI Engineer to join our core engineering team.

About the Role

We are building AI agents that can operate core CFO workflows end-to-end — including accounts payable, month-end close, reconciliation, record-to-report, vendor management and compliance workflows.

These agents don't just recommend actions. They execute finance processes by interpreting documents, applying accounting and control logic, performing transactions in enterprise systems such as SAP, and escalating genuine exceptions for human review.

As a Senior AI Engineer, you will own systems end-to-end, from agent architecture and orchestration to production deployment, evaluation, reliability and scale.

Key Responsibilities

1. Build Production AI Agents

• Design and build autonomous agents for finance workflows such as invoice-to-pay, three-way matching, reconciliation, GL close and exception handling.
• Build tool and action layers over ERP systems and enterprise applications.
• Design approval workflows, human-in-the-loop checkpoints and segregation-of-duty controls.
• Build robust error recovery and ensure complete auditability of agent actions.
• Integrate with SAP APIs, ERP interfaces, documents and file-based systems.

2. Agent Memory & Knowledge Systems

• Design memory and context strategies for long-running agents.
• Work on context windowing, summarization, compaction, retention and forgetting policies.
• Build finance knowledge graphs covering vendors, invoices, POs, GRNs, GL accounts, contracts, approvals and provenance.
• Work with graph databases such as Neo4j alongside vector search.

3. Evaluation & Model Strategy

• Build and improve evaluation frameworks for production AI systems.
• Create golden datasets, workflow-level metrics and regression gates.
• Develop automated evaluation and judging systems calibrated against human review.
• Monitor model performance and detect drift on live workloads.
• Work across OpenAI, Anthropic, Google and open-weight models.
• Design model routing based on quality, latency and cost, including cascading and provider failover.

4. Scale & Engineering

• Own the latency, throughput and unit economics of AI workflows at scale.
• Monitor and improve p50/p95/p99 latency and cost-per-workflow.
• Build and maintain APIs using FastAPI and REST.
• Mentor engineers working on AI systems and contribute to engineering best practices.

Required Experience

• 5+ years of software/ML engineering experience, including 2+ years shipping LLM systems in production.
• Proven experience building and deploying multi-step AI agents that perform real actions in production systems.
• Strong understanding of agent failure modes, reliability and control mechanisms.
• Experience designing memory and context strategies for long-running agents.
• Hands-on experience building an LLM evaluation framework, including datasets, metrics and automated judging.
• Experience working with at least two LLM model providers or model families.
• Experience migrating or operating live LLM workloads in production.
• Experience operating AI/ML systems at significant scale with measurable production outcomes.

Technical Qualifications

• Bachelor's/Master's degree in Computer Science, Computer Engineering or a related field.
• Advanced proficiency in Python.
• Strong experience with modern AI/ML development practices.
• Hands-on experience with LangGraph, LangChain, OpenAI Agents SDK, or custom agent orchestration.
• Working knowledge of graph databases such as Neo4j, Neptune or ArangoDB.
• Understanding of graph data modelling, schema design and Cypher/Gremlin.
• Strong API and data engineering fundamentals.
• Experience with FastAPI, PostgreSQL and vector databases.
• Experience deploying cloud-native applications on AWS, GCP or Azure.

Preferred Experience

• Finance, accounting, audit or ERP/SAP domain exposure.
• Experience with computer-use or browser automation agents.
• Knowledge of GraphRAG and ontology design.
• Experience with document AI/OCR.
• Fine-tuning or serving open-weight models.
• LLM observability and tracing using tools such as LangSmith, Langfuse or OpenTelemetry.
• Understanding of enterprise AI security, governance and compliance.
• Experience using AI coding agents as part of everyday software development.

Our Tech Stack

Python | LangGraph | LangChain | OpenAI | Anthropic | Google AI | FastAPI | PostgreSQL | pgvector | Neo4j | Elasticsearch | Java/Spring Boot | React | GCP | AWS | Docker

Familiarity with our exact stack is helpful but not mandatory if you have strong experience with equivalent technologies.

What You'll Work On

You will directly contribute to building AI systems that:

• Execute real finance workflows instead of simply generating recommendations.
• Interact with enterprise systems such as SAP.
• Process large volumes of financial documents and transactions.
• Make finance operations more automated and efficient.
• Operate with strong controls, auditability and human oversight.
• Serve enterprise customers at scale.

Why Join CashFlo?

• Build from the ground up: Be part of a new AI vertical targeting the global market.
• High-impact work: Your systems will operate on real enterprise finance workflows at scale.
• Strong engineering culture: Work alongside experienced engineers and leadership from top consulting, technology and finance backgrounds.
• Ownership: Own problems end-to-end rather than individual components.
• Growth: Opportunity to work across AI agents, LLMs, enterprise systems, evaluation and production infrastructure.
• Compensation & Equity: Competitive compensation, incentive structures and meaningful equity in an early-stage company.