Location: Jaipur, Rajasthan, India (On-site/Hybrid)
Experience: 0–1 years (Freshers welcome)
Employment Type: Full-Time
Function: Software Developer / Applied AI
About the Role
We build enterprise-grade lending infrastructure — Loan Origination, Loan Management, Collections, Co-Lending, and Financial Accounting systems — used by NBFCs, MFIs, HFCs, and BNPL providers. As we extend this platform with AI-driven automation , we're looking for a fresher Python Developer to join our Data Engineering team.
This is a hands-on role focused on preparing, cleaning, and structuring financial data (loan, KYC, repayment, collections, transaction data) so it can reliably feed ML models, LLM pipelines, and RAG/AI applications. You'll work at the intersection of data engineering and applied AI — not pure research, but production pipelines that touch real money movement and regulated data.
What You'll Work On
- Data pre-processing pipelines — cleaning, normalizing, and validating structured (loan schedules, ledgers) and semi-structured (KYC docs, agent notes, SMS/call transcripts) fintech data for downstream AI use
- LLM/AI project support — building data pipelines that feed prompt pipelines, RAG systems, and embedding generation for use cases like loan document summarization, collections call analysis, and underwriting assistance
- ETL/ELT scripting — writing Python jobs to move data between our LOS/LMS databases, data warehouse, and AI services
- Document intelligence — pre-processing scanned KYC documents, bank statements, and agreements (OCR output cleanup, chunking for embeddings, PII masking)
- Data quality & validation — building checks to catch inconsistencies before they reach financial or AI systems (a bad row in a repayment dataset breaks both reconciliation and model output)
- Collaboration with backend teams — working alongside Java/Spring Boot engineers to understand loan/accounting data models (Fineract-style entities: loans, clients, transactions, GL accounts) before designing pipelines around them
Must-Have Skills
- Solid fundamentals in Python (data structures, OOP basics, writing clean/testable scripts)
- Working knowledge of Pandas / NumPy for data manipulation
- Basic SQL — joins, aggregations, filtering on relational data
- Understanding of REST APIs (consuming and integrating with them)
- Familiarity with Git and version control workflows
- Conceptual understanding of ML/AI basics — what embeddings, vector search, and LLM prompting are, even without production experience
- Strong problem-solving ability and attention to detail (critical when working with financial data — a silent data error here has downstream accounting impact)
Good-to-Have (Not Mandatory for Freshers)
- Exposure to LangChain / LlamaIndex, OpenAI/Anthropic APIs, or vector databases (Pinecone, Weaviate, pgvector)
- Academic/project experience with NLP (tokenization, text classification, NER)
- Familiarity with Docker basics
- Exposure to AWS (S3, basic ECS/Lambda concepts)
- Any personal/academic project involving fintech, credit scoring, or document processing
- Basic understanding of Java/Spring Boot (useful since our core platform is Fineract-inspired, though not required day one)
Qualifications
- B.Tech/B.E. in Computer Science, IT, Data Science, or related field (2025/2026 graduates preferred)
- Strong academic or personal project portfolio (GitHub link expected) demonstrating Python + data work
- No prior professional experience required — we'll train on our domain (lending, accounting, regulatory data handling)
Why This Role Is Different From a Generic "Python Fresher" Job
- You'll work with real regulated financial data (loan lifecycles, repayments, KYC) — not toy datasets — so you'll learn data quality discipline that matters (auditability, correctness, PII handling under India's DPDP Act)
- Direct exposure to how AI is being applied inside core lending workflows, not just chatbots — underwriting support, collections prioritization, document automation
- Mentorship from engineers working on a Fineract-style, multi-tenant, API-first platform at scale
What We Offer
- Structured onboarding into both the fintech domain (loans, accounting, collections) and our AI tooling stack
- Exposure across the full stack: data engineering → AI pipelines → production integration with LOS/LMS
- Growth path into ML Engineering, Data Engineering, or AI Platform roles