Role Summary

You will design, build, and operate AI-powered systems that process mortgage and structured credit documents at scale - spanning classification, extraction, splitting, and compliance tagging. In parallel, you will partner with our engineering architect to embed AI-assisted developer productivity tools (code generation, automated PR review, test coverage analysis) across a 50-engineer org. This is a dual-track role: product-facing AI systems and platform engineering for AI adoption.

Key Responsibilities

- Design and maintain intelligent document pipelines for mortgage artefacts - loan packages, appraisals, title reports, closing disclosures - covering classification, splitting, field extraction, and audit tagging using LLMs + OCR.
- Build and optimise RAG (Retrieval-Augmented Generation) workflows for structured credit data including chunking strategies, embedding models, and vector store management.
- Collaborate with the architect to evaluate and integrate AI coding assistants (GitHub Copilot, Cursor, Claude Code), automated PR review bots, and test-case generation pipelines into existing Forgejo/Jenkins CI/CD workflows.
- Develop prompt engineering standards, fine-tuning strategies, and model evaluation frameworks aligned with internal SDLC quality gates.
- Own MLOps infrastructure: model versioning, A/B testing, drift detection, and observability on GCP (Vertex AI, Cloud Run, BigQuery ML).
- Drive data analytics initiatives - building LLM-powered reporting layers, anomaly detection, and insights extraction from loan performance data.
- Establish AI engineering best practices, conduct technical spike reviews, and mentor junior/intern engineers.
- Stay ahead of LLM landscape shifts (open-weight models, multimodal, agents) and present actionable adoption proposals to leadership.

Experience

- 4-7 years experience
- GCP
- LLMs RAG MLOps
- Mortgage Domain

Required Skills

- AI / ML Core:



LLM APIs (Claude / Grok / Gemini) RAG Pipelines Prompt Engineering Fine-tuning (LoRA/QLoRA) Vector Stores (pgvector / Qdrant) Embeddings LangChain / LlamaIndex Evaluation Frameworks
- Platform & Infrastructure: GCP (Vertex AI, Cloud Run, GCS) Python (FastAPI / Flask) Node.js Docker / Kubernetes Jenkins Pipelines BigQuery / BigQuery ML PostgreSQL Grafana / Loki / Tempo
- Document Intelligence: Document Classification Information Extraction (NER/NLP) PDF Parsing Multimodal LLMs Google Document AI OCR (Tesseract)
- Developer Productivity AI: AI Code Review Automation Test Generation (LLM-assisted) MCP / Claude Code SonarQube Integration Forgejo Webhooks

Expectations At Level

- Independently scope and deliver AI features end-to-end: from data ingestion to deployed API to monitoring dashboard.
- Lead design reviews for AI components; document architectural decisions with explicit trade-off analysis.
- Demonstrate measurable impact - extraction accuracy, latency SLOs, developer time saved - with quantified baselines.
- Proactively identify risks in LLM outputs (hallucination, PII leakage, compliance edge cases) and implement mitigation guardrails.
- Contribute to org-wide AI adoption playbook and internal knowledge base.

NICE TO HAVE

Prior exposure to mortgage, structured credit, or financial services document workflows. Experience with agentic AI frameworks (LangGraph, AutoGen). Published models on Hugging Face or open-source AI contributions. Familiarity with Apache Superset or BI tooling for analytics layers.

We are considering only Immediate Joiners or candidates who are currently serving their notice period with 15 days or less remaining.

Location

Bangalore

Work Mode

On-site

Work Schedule

Flexible working hours

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Sr AI Engineer (Bengaluru)
🏢 FinacPlus
📍 Bengaluru