Full Stack AI Software Engineer

🕒 il y a 16 jours

🇺🇸 États-Unis – Télétravail

⏰ Temps Plein

🟡 Intermédiaire

🟠 Senior

🧑‍💻 Développeur Full-Stack

👻 Score fantôme 10%

Ce que ça veut dire : la probabilité que cette offre reste ouverte sans recrutement actif.Comment le lire : 60 % ou plus, on pense que c'est une offre fantôme. Un score plus bas est moins inquiétant.Comment on la calcule : ancienneté, republications, postes ouverts vs taille de l'équipe, rapidité de clôture, et si le salaire est indiqué.À noter : une estimation à partir de données publiques, pas un jugement sur l'entreprise. De vraies offres peuvent avoir un score élevé, alors prenez-le comme un indice, pas comme un fait.

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Freedom Mortgage

Site WebLinkedInTous les Emplois

5001 - 10000 employés

Fondée en 1990

💸 Finance

🏠 Immobilier

👥 B2C

💰 €500 000 000 Debt Financing - Freedom Mortgage en 2025-08

Finance • Real Estate • B2C

Freedom Mortgage est un prêteur et gestionnaire de prêts hypothécaires résidentiels basé aux États-Unis qui offre des produits de prêts immobiliers et des services connexes aux consommateurs. L'entreprise propose des prêts hypothécaires pour l'achat et le refinancement (VA, FHA, USDA et conventionnels), des HELOCs, des termes à taux fixe et ajustable, des refinancements avec retrait de trésorerie et simplifiés, des outils de gestion de compte en ligne et de paiement, ainsi qu'un support client comprenant des conseillers en prêt. Freedom Mortgage se concentre sur l'origine et le service des prêts hypothécaires pour les consommateurs (B2C) pour les acheteurs de maison et les propriétaires.

Description

• Partner with business stakeholders to identify problems, define outcomes, and engineer technical solutions • Translate business needs into requirements, technical designs, user stories, acceptance criteria, and production-ready software • Rapidly prototype, demonstrate, test, and iteratively release solutions with users • Build and enhance full-stack applications across frontend, backend, APIs, integrations, data, and infrastructure • Design and implement AI-enabled capabilities using LLMs, RAG, embeddings, vector search, tool calling, multimodal models, and agentic workflows • Build secure AI agents that interact with applications, APIs, databases, documents, and enterprise systems • Develop prompts, system instructions, context-management strategies, tools, workflows, and reusable AI skills • Evaluate models and implement evaluations, regression tests, guardrails, human-review controls, grounding, observability, and fallback mechanisms • Own software delivery from discovery and architecture through development, testing, deployment, production support, and continuous improvement • Write clean, modular, testable, secure, and documented production code • Develop and maintain automated unit, integration, API, UI, regression, performance, security, and end-to-end tests • Build and maintain CI/CD pipelines and release software through safe, observable, reversible deployments • Serve as accountable engineer or technical owner for up to three applications • Monitor application availability, performance, capacity, security, errors, and user experience • Diagnose production incidents and perform maintenance, upgrades, vulnerability remediation, and technical-debt reduction • Maintain application documentation, runbooks, architecture diagrams, deployment instructions, and recovery procedures • Participate in incident response, root-cause analysis, disaster-recovery testing, and business-continuity exercises • Collaborate with information security, enterprise architecture, data-management, infrastructure, cloud, network, identity, DevOps, QA, product, legal, risk, and compliance teams • Ensure compliance with software-development, security, architecture, data-governance, privacy, accessibility, records-management, and release-management standards • Contribute reusable engineering patterns, libraries, templates, prompts, skills, and development standards • Mentor engineers and help improve AI-enabled software delivery standards

🎯 Exigences

• Five or more years of professional software-engineering experience across design, development, testing, deployment, and support of production applications • At least two to three years of professional experience using modern AI-assisted engineering tools and AI development practices • Demonstrated ability to build and support full-stack applications from concept through production • Strong proficiency in at least one backend language: Python, Java, C#, Go, or TypeScript/Node.js • Strong proficiency in JavaScript or TypeScript and a frontend framework such as React, Angular, or Vue • Experience with RESTful APIs, event-driven services, microservices, and enterprise integrations • Experience with relational databases, SQL, data modeling, and a nonrelational or vector database • Practical experience integrating LLMs or generative-AI services into production or enterprise applications • Experience with AI coding tools such as Cursor, Claude Code, GitHub Copilot, OpenAI Codex, or comparable platforms • Experience with Git, automated testing, code review, CI/CD, and modern DevOps practices • Experience deploying and operating applications in AWS, Microsoft Azure, or Google Cloud Platform • Working knowledge of containers, infrastructure as code, observability, application security, and vulnerability remediation • Strong understanding of secure software-development principles, authentication, authorization, encryption, secrets management, dependency management, and application-security risks • Ability to work directly with business users and convert business problems into working software • Strong analytical, troubleshooting, communication, collaboration, and documentation skills • Ability to manage multiple priorities and own up to three applications • Bachelor’s or master’s degree in computer science, software engineering, information systems, data science, artificial intelligence, or related discipline, or equivalent practical experience • Experience with AI agents, RAG, semantic search, document processing, workflow automation, model evaluation, prompt testing, AI observability, red-team testing, and responsible-AI controls • Experience with LangGraph, Semantic Kernel, OpenAI Agents SDK, model context protocol, or comparable orchestration technologies • Experience with Kubernetes, serverless architectures, messaging platforms, API gateways, and distributed systems • Experience in a regulated industry such as financial services, mortgage lending, banking, insurance, healthcare, or government • Familiarity with privacy, model risk, information security, records retention, accessibility, and regulatory compliance requirements • Experience modernizing legacy applications and integrating them with cloud, data, and AI platforms • Experience collaborating with product designers, business operators, and end users • Certificates, licenses, and registrations: None required

🏖️ Avantages

• Remote work arrangement • Equal employment opportunities • Reasonable accommodations for disabilities • Opportunity to work on modern AI-enabled software and enterprise applications