
11 - 50 employees
Founded 2022
đł Fintech
đ€ Artificial Intelligence
âïž SaaS
Fintech âą Artificial Intelligence âą SaaS
Saaf Finance is an AI-driven underwriting platform for mortgage lenders. Saafâs AI Underwriter automates mortgage underwriting across products such as Non-QM, RTL, HELOC, DSCR, Jumbo, and Bank Statement loans, using OCR, large language models, and API integrations with data vendors and loan origination systems (e. g. , Encompass, MeridianLink). The platform produces structured âSource of Truthâ loan data, reduces fraud risk through direct data connections, and claims substantial operational gains (faster processing, lower cost, and greater scalability). Saaf positions itself as a B2B partner to mortgage operations and secondary market participants, leveraging a training set of 200K+ loans and decades of industry experience to augment underwriters rather than replace them.
đ September 10
đźđł India â Remote
â° Full Time
đĄ Mid-level
đ Senior
đ€ Machine Learning Engineer
đ» Ghost score 25%
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11 - 50 employees
Founded 2022
đł Fintech
đ€ Artificial Intelligence
âïž SaaS
Fintech âą Artificial Intelligence âą SaaS
Saaf Finance is an AI-driven underwriting platform for mortgage lenders. Saafâs AI Underwriter automates mortgage underwriting across products such as Non-QM, RTL, HELOC, DSCR, Jumbo, and Bank Statement loans, using OCR, large language models, and API integrations with data vendors and loan origination systems (e. g. , Encompass, MeridianLink). The platform produces structured âSource of Truthâ loan data, reduces fraud risk through direct data connections, and claims substantial operational gains (faster processing, lower cost, and greater scalability). Saaf positions itself as a B2B partner to mortgage operations and secondary market participants, leveraging a training set of 200K+ loans and decades of industry experience to augment underwriters rather than replace them.
âą Own the infrastructure supporting production AI systems âą Re-architect AI services from single-host deployments to horizontally scalable, orchestrated infrastructure âą Design infrastructure for LLM workloads with long-running requests, streaming responses, bursty concurrency, expensive downstream calls, and upstream rate limits âą Own capacity, autoscaling, and unit economics âą Build development-to-pre-production-to-production promotion paths with consistent, reproducible environments âą Version-control and make infrastructure, configuration, and application logic reviewable âą Build CI/CD with fast deployment, rollback, staged rollout, and auditable change history âą Build self-serve tooling for engineers and technically minded teammates to define, modify, and test AI workflow logic âą Design validation, versioning, review, staged promotion, and recovery guardrails âą Instrument the AI stack for latency, throughput, failure modes, cost, and output quality âą Build meaningful alerting and incident practices that improve the system âą Own secrets, access control, and environment isolation in a regulated industry
âą Production infrastructure ownership: deployed and operated containerized services in production under real traffic, including rollouts, autoscaling, failure isolation, resource limits, and rollback âą Infrastructure as code; declarative and reproducible environments âą CI/CD experience including automated testing, environment promotion, safe rollout, and fast recovery âą Strong Python; ability to read and change application code, profile it, and fix it âą Operational judgment across application, network, and infrastructure boundaries âą End-to-end ownership of design, implementation, deployment, monitoring, and follow-up âą Fast, incremental iteration and risk reduction âą Experience operating LLM or ML workloads in production âą Cloud deployment experience, AWS preferred âą Experience in fintech, lending, insurance, or another regulated industry âą Experience building internal developer platforms or self-serve tooling âą Full-stack comfort for lightweight UIs or internal tools âą Experience designing multi-environment promotion pipelines âą Familiarity with agent orchestration frameworks âą Observability for non-deterministic systems âą Inference optimization including model serving, batching, caching, and cost reduction âą Exposure to workflow automation tooling and low-code builders
âą Competitive compensation âą Unlimited PTO âą Remote-first with flexible hours âą $2,000/year professional development budget âą Home office setup stipend
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