AHL - Saaf AI

Role Description Saaf AI is hiring a Mortgage Operations Analyst to join our growing U.S. mortgage operations team. In this role, you will review and support residential mortgage loan files across the full loan lifecycle — from initial disclosures and processing through closing and post-close — to ensure accuracy, completeness, compliance, and adherence to investor and lender guidelines. This is a hands-on operations role, not a passive audit function. You will work inside the loan file, validate borrower documentation, surface discrepancies, calculate income and assets, resolve underwriting conditions (including those flagged by AI-driven underwriting systems), and collaborate with processing, underwriting, closing, and post-closing teams. The ideal candidate is detail-oriented, takes ownership, learns quickly, and can work independently in a remote environment. Key Responsibilities - Review residential mortgage loan files at every stage of the loan lifecycle, including pre-closing and post-close. - Validate borrower documentation: income (W-2, self-employed, bank statement, DSCR), assets, credit, collateral, and supporting documents. - Calculate qualifying income across different documentation types and validate reserves and source of funds. - Review and prepare Initial Disclosures for accuracy, completeness, and regulatory compliance. - Identify missing documentation, data discrepancies, compliance gaps, and process issues — and resolve them with the right stakeholders. - Respond to and clear underwriting conditions, including those flagged by AI-driven underwriting systems. - Work directly inside Encompass LOS to review, update, and validate loan information; manage milestones and order services. - Communicate clearly with internal teams and, where required, with borrowers to collect outstanding documentation. - Document findings and actions accurately in internal systems. - Contribute to continuous process improvement and quality assurance efforts. Qualifications - 2+ years of hands-on U.S. mortgage operations experience across processing, underwriting support, loan review, closing, or post-close functions. - Working knowledge of the full U.S. residential mortgage loan process — not pigeonholed into a single task or document type. - Hands-on experience with Encompass LOS (required — will be validated in a live test). - Familiarity with mortgage documentation, disclosures, and loan file review. - Ability to calculate income across different employment types (wage-earner, self-employed, bank statement) and validate assets and reserves. - Strong attention to detail, analytical skills, and problem-solving mindset. - Excellent written and verbal English communication skills — must comprehend complex instructions and communicate clearly with U.S.-based teams. - Proficiency in Microsoft Excel and Microsoft Office. - Ability to manage multiple loan files independently and meet deadlines in a fast-paced remote environment. Preferred Qualifications - Exposure to Non-QM, DSCR, or RTL loan products (priority). - Exposure to Agency, Government, or Conventional loan products. - Prior experience with U.S. lenders or brokers (Better, Rocket, Quattro, Quality Source, Ulti Source, or similar). - Experience reviewing Initial Disclosures and mortgage compliance documentation. - Understanding of TRID, RESPA, TILA, or other mortgage regulatory frameworks (a plus, not required). - Prior remote mortgage operations experience. Ideal Candidate - Trainable and eager to learn new mortgage products, regulations, and processes. - Organised, detail-focused, and quality-driven. - Strong critical thinker — challenges AI-generated outputs and underwriting condition recommendations when they don't make sense. - Comfortable working independently while collaborating with cross-functional teams. - Takes ownership of assigned work and follows through without constant supervision. Compensation & Benefits - Competitive compensation - Unlimited PTO - Remote-first - Home office setup stipend

Role Description Saaf AI is building the future of mortgage lending by combining cutting-edge AI with robust data infrastructure. As part of a top-10 private lender processing billions in loan volume, backed by leading asset managers and funds, we are growing fast — and data and AI are at the center of everything we build. We don’t just experiment with AI — we integrate it deeply into how we operate. Our systems rely on scalable data pipelines, structured data models, and real-time workflows that power underwriting, document processing, and borrower interactions. AI is embedded across these layers, from data extraction and validation to intelligent automation. If you’re excited about building high-quality data systems in an AI-native environment — where data pipelines, automation, and intelligent workflows come together — you’ll fit right in. Key Responsibilities - Data Pipeline Development - Design, implement, and maintain ETL/ELT pipelines for structured and unstructured datasets from internal and external sources. - Leverage AI-assisted development tools to accelerate pipeline authoring, generate transformation logic, and automate boilerplate code. - Data Warehousing & Modeling - Build and optimize data warehouses and marts (Snowflake, BigQuery, or similar) for analytics, reporting, and product use cases. - Design, implement, and maintain conceptual, logical, and physical data models to ensure scalable, consistent, and high-quality datasets for downstream analytics and applications. - Integration & Ingestion - Ingest data from APIs, SaaS platforms (CRM, financial data APIs), and internal systems into the core data platform. - Build and maintain reliable connectors and ingestion frameworks that handle schema evolution, rate limits, and error recovery. - Data Quality & Governance - Implement validation, schema management, and robust documentation to ensure data accuracy and compliance. - Use AI tools to support data profiling, anomaly detection, and automated documentation of data lineage and transformations. - AI-Integrated Data Engineering - Use AI-assisted tools (code generation, intelligent autocomplete, automated testing) as a regular part of your data engineering workflow. - Evaluate and integrate emerging AI tools and practices into the team's data development process. - Build and support agentic workflows and multi-step automated processes that act on data in real time, including AI-powered data validation and enrichment. - Apply AI-assisted analysis to debugging pipeline failures, optimizing query performance, and identifying data quality issues. - Performance & Reliability - Monitor and fine-tune pipeline and warehouse performance for scalability and cost efficiency. - Set up logging, monitoring, and alerting for data jobs to ensure reliability and fast incident response. - Security & Compliance - Apply data security and privacy controls aligned with financial regulatory requirements, ensuring full traceability of every transformation. - Foster a security-first mindset across all data operations. - Analytics Enablement - Provide clean, consistent datasets for analysts, product managers, and operational teams to support fast, data-driven decisions. - Collaborate closely with product managers, data scientists, and full stack engineers to align data models with business needs. Qualifications - 5+ years in a data engineering or similar backend data-focused role. - Strong SQL and Python development skills for data transformation and automation. - Experience with modern ETL/ELT frameworks such as dbt. - Proficiency with cloud platforms (AWS preferred) and serverless data services. - Strong experience with data warehouse technologies (Snowflake preferred). - Skilled in API integrations and ingestion from third-party systems. - Proficient in data modeling (Kimball/Star schema, Data Vault). - Demonstrated, regular use of AI-powered development tools (e.g., Cursor, GitHub Copilot, Claude Code, or similar) to accelerate data pipeline development, debugging, or documentation. - Proven track record of delivering production-grade data pipelines at scale. - Experience implementing CI/CD practices for data workflows. - Experience collaborating closely with product managers, data scientists, and full stack engineers. - Startup mindset: hands-on, resourceful, and comfortable operating in a fast-paced environment. Preferred - Experience building agentic workflows and orchestrating multi-step automated processes that act on data in real time. - Familiarity with data engineering patterns and infrastructure required for AI-powered tools and automation platforms. - Experience working with financial datasets and APIs in a high-compliance environment. - Understanding of data privacy regulations such as GDPR and CCPA. - Experience with prompt engineering for code generation, data transformation logic, or building AI-powered data workflows. Benefits - Competitive salary - Unlimited PTO - Remote-first with flexible hours - Up to $2,000/year professional development budget - Home office setup stipend

Role Description As a Full Stack Engineer at Saaf Finance, you will design and ship software that automates complex mortgage workflows while collaborating closely with founders, engineers, and design. We are an AI-native engineering team: AI-assisted development tools are a regular part of how we build, review, and ship software. We expect engineers to use these tools thoughtfully and effectively as part of their daily workflow. Responsibilities - Product & Engineering - Design, develop, and maintain scalable software solutions that automate mortgage-related processes and support collaboration among end users. - Work closely with founders, senior engineers, and design teams to translate customer needs into effective, practical software. - Shape engineering strategy and contribute to new product features and team planning. - Architecture & APIs - Design and maintain scalable systems, APIs, and services that are easy to update, expand, and secure. - Design, implement, and document RESTful and/or GraphQL APIs that are robust, secure, and easy to use. - Ensure data integrity, security, and governance across applications, following best practices for data management and compliance. - Foster a security-first mindset in all development activities. - AI-Integrated Development - Leverage AI-assisted development tools (code generation, intelligent autocomplete, automated refactoring) as a regular part of your workflow to accelerate delivery and improve code quality. - Use AI tools to support documentation, code review, test generation, and knowledge sharing across the team. - Evaluate and integrate emerging AI development tools and practices, helping the team continuously improve how we build software. - Apply AI-assisted analysis to debugging and troubleshooting workflows where appropriate. - Team & Process - Break down and organize large projects into manageable tasks, ensuring clarity and alignment within the team. - Encourage documentation and knowledge sharing to promote collaboration and onboarding. - Create an environment where everyone feels safe to express ideas and disagreements, and is open to new perspectives. - Ensure team discussions focus on organizational goals and strategies. - Regularly review and improve team methods and workflows. - Guide and support team members, helping to build skills and ensure mutual coverage. - Reliability & Operations - Use thorough debugging and troubleshooting methods to resolve software issues. - Set up and enhance monitoring to improve service stability and performance. Qualifications - 5+ years of experience as a Full Stack Developer, with experience spanning both frontend and backend development. - 3+ years of experience working with frontend JavaScript frameworks such as React or Angular, and backend technologies like NodeJS (these may overlap). - Strong experience designing and implementing APIs (RESTful and/or GraphQL), including versioning, documentation, and security best practices. - Demonstrated, regular use of AI-powered development tools (e.g., Cursor, GitHub Copilot, Claude Code, or similar) to accelerate coding, debugging, or documentation workflows. - Proficiency in SQL and NoSQL databases such as Postgres, MongoDB, or MySQL. - Solid understanding of the software development life cycle and Agile methodologies. - Proven experience leading a team or architecting a large-scale enterprise product. - Exceptional problem-solving, debugging, and software design skills. - Excellent written and verbal communication skills. - Self-driven with a strong work ethic and passion for excellence. - Ability to work effectively both independently and as part of a team. Preferred - Strong understanding of AWS infrastructure and hands-on experience with services such as Serverless Framework, Lambda, and CloudFormation. - Experience working with Terraform. - Experience with prompt engineering for code generation, refactoring, test creation, or building AI-powered product features. - Knowledge of design patterns, data structures, and distributed systems. - Experience with data engineering (pipelines, ETL workflows, data architecture). - Prior early-stage startup experience is highly preferred. Benefits - Competitive salary - High ownership from day one — your work will directly shape core systems and products - Fast-paced environment with quick decision cycles and minimal bureaucracy - Remote-first team with flexibility on work hours and location - Direct access to founders and cross-functional teams — no layers, no silos - Clear expectations, regular feedback, and support for professional growth - Work on real problems in a complex, high-impact industry

Role Description Saaf AI is building the infrastructure backbone for modern mortgage operations by combining advanced AI with scalable, reliable systems. As a Senior DevOps Engineer, you will own the infrastructure, deployment pipelines, and reliability practices that support our platform and enable engineering teams to ship quickly and safely. You will design and operate scalable systems, improve observability, and ensure high availability across critical workflows. We are an AI-native engineering team, where AI-assisted tools are a regular part of how we build, deploy, and maintain infrastructure. From writing infrastructure code to debugging production issues and optimizing system performance, you will use these tools to improve efficiency and reliability. You will also support the infrastructure required to run AI-driven workflows in production, ensuring they are robust, scalable, and maintainable. Key Responsibilities - Infrastructure & Cloud Operations - Design, build, and maintain production-grade AWS infrastructure using Infrastructure-as-Code (Terraform preferred). - Architect and manage serverless and containerized environments that balance cost, performance, and reliability. - Implement and maintain networking, security groups, IAM policies, and cloud resource configurations following least-privilege principles. - CI/CD & Deployment - Own and evolve the CI/CD pipeline ecosystem, primarily using GitHub Actions, to enable fast, safe, and repeatable deployments. - Implement deployment strategies (blue-green, canary, rolling) that minimize risk and downtime. - Automate build, test, and release workflows across multiple services and environments. - AI-Integrated DevOps - Leverage AI-assisted tools (code generation, intelligent autocomplete, automated IaC authoring) as a regular part of your infrastructure workflow to accelerate delivery and reduce configuration errors. - Use AI tools to support incident diagnosis, log analysis, runbook generation, and documentation of infrastructure decisions. - Evaluate and integrate emerging AI tools and practices into the team's DevOps processes. - Build and support the infrastructure layer for agentic workflows, including compute orchestration, autoscaling, and cost-efficient execution of AI-powered automation. - Monitoring, Observability & Incident Management - Design and maintain monitoring, logging, and alerting systems that provide clear visibility into platform health and performance. - Implement distributed tracing and structured logging across services and multi-step workflows. - Lead incident response, conduct post-mortems, and drive reliability improvements based on findings. - Security & Compliance - Apply cloud security best practices across all infrastructure, including secrets management, encryption, network segmentation, and access controls. - Design secure secrets and configuration management for agentic processes, including API keys, model tokens, and external service credentials. - Ensure infrastructure meets financial regulatory and compliance requirements with full auditability. - Data Infrastructure Support - Support and maintain infrastructure for data engineering workflows, including Snowflake environments, ETL/ELT pipelines, and dbt execution. - Manage serverless event-driven pipelines and orchestration tools (Step Functions, Temporal, or similar). - Team & Process - Collaborate with product engineers, data engineers, and founders to ensure infrastructure supports rapid iteration and reliable delivery. - Document infrastructure decisions, runbooks, and operational procedures to support team knowledge sharing and onboarding. - Regularly review and improve operational workflows, automation coverage, and infrastructure cost efficiency. Qualifications - 4+ years of experience in DevOps, SRE, or similar infrastructure-focused roles. - Proficient in AWS with strong Infrastructure-as-Code experience (Terraform preferred). - Strong CI/CD expertise with GitHub Actions. - Experience with containerization and serverless architectures. - Skilled in monitoring, logging, and incident management. - Strong scripting and automation skills in Bash, Python, or Node.js. - Knowledge of cloud security principles, least privilege, and compliance requirements. - Experience with Snowflake and data engineering workflows (ETL, dbt). - Exposure to Kubernetes and orchestration tools. - Understanding of serverless event-driven pipelines (Step Functions, Temporal). - Demonstrated, regular use of AI-powered development tools (e.g., Cursor, GitHub Copilot, Claude Code, or similar) to accelerate infrastructure authoring, debugging, or documentation workflows. - Startup mindset: hands-on, resourceful, and comfortable operating in a fast-paced environment. Preferred - Experience with event-driven workflow orchestration tools such as Step Functions, Temporal, Airflow, or Prefect. - Familiarity with agentic workflow patterns, including integrating API-based decision points, asynchronous task handling, and dynamic routing of requests. - Understanding of infrastructure requirements for AI-powered automation, including latency optimization, autoscaling strategies, and cost-efficient compute for high-throughput processes. - Ability to design secure secrets and configuration management systems for agentic processes, including API keys, model tokens, and external service credentials. - Experience implementing observability for multi-step workflows, including distributed tracing, structured logging, and audit-friendly data pipelines. - Experience with prompt engineering for IaC generation, incident analysis, or building AI-powered operational tooling. - Prior early-stage startup experience is highly preferred. Benefits - Competitive salary - Unlimited PTO - Remote-first with flexible hours - Yearly professional development budget - Home office setup stipend