Principal Engineer

Location: Coimbatore

Experience: 8+ Years

Notice: Immediate Joiner

The Mission: Simplifying Bookkeeping with AI

FluxBooks is an AI-powered bookkeeping workspace that helps CPAs and bookkeepers turn

raw bank, credit card, and loan transactions into clean, categorized, and document-backed

records ready for QuickBooks and reconciliation.

By bringing statement review, document matching, categorization, client clarification, and

transaction posting into a single workflow, FluxBooks eliminates the need to switch between

spreadsheets, emails, receipts, and accounting software.

Rather than replacing accounting judgment, FluxBooks reduces the manual effort involved in

collecting documents, matching transactions, and identifying exceptions, enabling

professionals to review and reconcile books faster and with greater confidence.

Core Responsibilities

• Engineering Architecture & System Design: Own the end-to-end design of backend

systems and APIs from data ingestion and processing to service deployment and

performance optimization. Architect scalable microservices, event-driven workflows,

and reusable platform components across client domains. Define enterprise

standards for API design, caching strategies, service communication patterns, and

integration pipelines.

• DevOps & Production Deployment: Establish repeatable patterns for deploying

Node.js, Java, and Python services in production. Drive automation through CI/CD

pipelines using Jenkins, GitHub Actions, and Terraform. Architect for multi-cloud

scalability across Azure, AWS, and GCP. Build strategic PoCs to validate technical

fitment and translate business problems into working software systems.

• Governance, Security & Compliance: Define and enforce AI architecture principles,

security policies, and responsible AI guardrails. Implement controls for PII/PHI

protection, hallucination risk mitigation, audit logging, and model explainability.

Apply zero-trust principles private networking, API gateways, and identity

management to keep data within secure perimeters.

• Collaboration & Technical Leadership: Partner with frontend, cloud, security, and

product teams for end-to-end architectural alignment. Lead build-vs-buy

assessments for platforms, databases, and DevOps tooling. Mentor engineers,

conduct architecture reviews, and track the evolving technology landscape to

recommend timely adoption of emerging frameworks and tools.

Technical & Professional Qualifications

• Technical Architecture Experience: 8 years in software engineering or platform

architecture, with hands-on experience designing and delivering scalable systems

using Node.js, Java, and Python.

• Node.js & Java Expertise: Deep hands-on experience with Node.js frameworks

(Express, NestJS) and Java ecosystems (Spring Boot, Microservices), with a proven

ability to architect and deploy high-performance backend systems at scale.

• Python & Engineering Depth: Strong command of Python for backend services,

automation, and data processing. Proficient with Docker, Kubernetes, and distributed

systems design across Azure, AWS, and GCP.

• Database & Storage Proficiency: Hands-on experience with relational databases

(PostgreSQL, MySQL), NoSQL (MongoDB, DynamoDB, Redis), and search engines

(Elasticsearch), with strong understanding of data modeling and query optimization

strategies.

• Analytical Thinking: Ability to evaluate technology trade-offs, define solution design

strategies, and translate complex business problems into scalable technical

architectures.

• Governance & Security Knowledge: Strong grasp of data governance, PII/PHI

handling, OAuth 2.0, zero-trust architecture, and responsible AI frameworks

applicable to enterprise environments.

• Soft Skills: Exceptional ability to communicate architectural decisions to both

technical teams and business stakeholders, with a focus on clarity, pragmatism, and

long-term system thinking.

Tech Stack

• Backend Languages & Runtimes: LangChain/LangGraph, OpenAI/Anthropic APIs,

Pydantic AI, Langfuse (observability), and VectorDB (Pinecone/Chroma).

• Deep Learning & Research Stack: REST, GraphQL, gRPC, Swagger/OpenAPI, Kafka,

RabbitMQ, WebSockets.

• Enterprise Production Stack: Python, TensorFlow/Keras, Docker, Kubernetes, AWS

SageMaker/Vertex AI.

• Data & Analytics Stack: Apache Spark (Databricks), Pandas, SQL, Kafk

Back End Developer