AI Software Engineer

• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field (or equivalent experience). \n

• 3-5+ years of software engineering experience building web or backend applications. \n

• Hands-on experience with machine learning or generative AI tools (OpenAI, Anthropic, HuggingFace, LangChain, vector databases, etc.). \n

• Proficiency in languages such as Python, TypeScript/JavaScript, or C#. \n

• Strong working knowledge of Microsoft Azure, including hands-on experience deploying, securing, and operating production workloads (compute, networking, identity, storage, and Key Vault)., * Solid experience with containers and Kubernetes (preferably AKS), including deployments, services, ingress, config/secrets, autoscaling, and debugging workloads with kubectl., * Familiarity with mortgage workflows: LOS, POS, underwriting, rate/pricing engines, and compliance considerations. \n

• Microsoft Azure certifications (e.g., AZ-204 Developer, AZ-104 Administrator, AZ-305 Solutions Architect, or AI-102 AI Engineer) and/or Kubernetes certifications (CKA or CKAD). \n

• Experience with infrastructure-as-code (Terraform or Bicep) and GitOps or Helm-based deployment workflows., * Experience deploying and scaling AI/LLM workloads on Azure (Azure OpenAI, AI Foundry, or self-hosted models on AKS)., * Strong problem-solving skills with the ability to map operational processes to technical solutions. \n

• Ability to work cross-functionally and communicate complex concepts to non-technical teams.

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• Architect, develop, and deploy AI-powered applications for internal operations (e.g., loan processing tools, underwriting automation, compliance checks, workflow optimization). \n

• Build customer-facing and marketing-facing software such as intelligent chat assistants, personalized loan recommendation engines, automated prequalification tools, and interactive calculators. \n

• Integrate AI models (LLMs, predictive models, generative AI) into new or existing products using modern development frameworks and APIs. \n

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Cloud Infrastructure & Deployment

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• Design, deploy, and operate applications on Microsoft Azure, using services such as Azure Kubernetes Service (AKS), App Service, Azure Functions, Azure SQL, Storage, Key Vault, and Azure OpenAI / AI Foundry. \n

• Containerize applications with Docker and run them on Kubernetes. This includes writing and maintaining manifests and Helm charts, and managing deployments, scaling, ingress, secrets, and resource limits. \n

• Build and maintain CI/CD pipelines (Azure DevOps or GitHub Actions) that take code from commit to production reliably and repeatably. \n

• Manage cloud resources with infrastructure-as-code (Terraform, Bicep, or ARM templates). \n

• Implement monitoring, logging, and alerting (Azure Monitor, Application Insights, Log Analytics) to keep production AI services healthy, and troubleshoot cluster and application issues when they come up. \n

• Apply cloud security best practices, including Entra ID (Azure AD) identity and role-based access, managed identities, network isolation, and secure secrets handling. All of this should meet the data-protection requirements for sensitive borrower information. \n

• Optimize cloud cost and performance for AI workloads, including model hosting, inference scaling, and GPU/compute usage. \n

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Collaborative Product Engineering

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• Partner with operations, loan officers, compliance, marketing, and customer experience teams to identify high-impact use cases for AI and translate them into scalable products. \n

• Work closely with data engineering to source, prepare, and validate datasets used for model training and evaluation. \n

• Develop secure, compliant, and well-documented systems aligned with mortgage regulatory requirements. \n

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Model Development & Optimization

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• Train, fine-tune, or customize AI models to improve accuracy, performance, and relevance to mortgage workflows. \n

• Implement continuous monitoring, versioning, and quality checks for AI models in production. \n

• Evaluate emerging AI technologies and recommend adoption paths for the organization. \n

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Technical Leadership & Best Practices

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• Establish development standards for AI-enabled software, including testing strategies, documentation, and model governance. \n

• Provide guidance on responsible AI usage, data protection, and fair lending considerations in all deployments. \n

• Contribute to long-term AI strategy to modernize the company’s tech stack and customer experience. \n

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\n, * Ownership of systems in production, from architecture through deployment, monitoring, and incident response. \n

• Curiosity and eagerness to explore new AI capabilities. \n

• Commitment to data security, fair lending, and responsible AI use. \n

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What This Role Enables

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• Faster, more accurate loan processing and internal workflows. \n

• Enhanced digital customer experience through AI-driven tools and automation. \n

• Scalable marketing technology that personalizes outreach and increases conversion. \n

• Secure, reliable, cloud-native infrastructure that lets AI products scale with the business. \n

• A forward-thinking, innovative culture that positions the company as a technology leader in the mortgage industry. \n