Senior AI Engineer

About FinacPlus :

FinacPlus is a Great Place to Work Certified organization - a recognition of our people first culture, collaborative environment, and focus on professional growth. We provide high-end virtual business process and technology services to leading global clients across finance, banking, mortgage, research, and data services. At FinacPlus, you'll work with world-class talent, cutting-edge technology, and international stakeholders - while still enjoying the openness, agility, and career visibility of a fast-growing company.

About Our Client - Toorak Capital Partners :

You will be part of a dedicated engineering team supporting Toorak Capital Partners, a leading U.S. - based integrated correspondent lending platform that funds residential, multifamily, and mixed-use real estate loans across the U.S. and U.K. Headquartered in Summit, New Jersey, Toorak's leadership team brings deep expertise across real estate lending, capital markets, securitization, asset management, and credit. To date, Toorak-funded projects have renovated or stabilized housing for 9,000+ families - averaging 500+ families every month. This is a rare opportunity to build mission-critical, cloud-based mortgage technology platforms that directly support one of the most respected players in the global mortgage finance industry.

Experience :

4 - 7 Years Experience GCP LLMs, RAG, MLOps, Mortgage Domain.

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).