Role Description The Tax Artificial Intelligence Manager leads the engineering team responsible for building and evolving an AI-enabled application platform that supports document processing, workflow automation, AI-assisted analysis, and internal and external user-facing applications for tax. This role sits at the intersection of software architecture and AI/technology execution, partnering with product owners, business subject matter experts (SMEs), security, and infrastructure teams to translate real “jobs to be done” into secure, scalable, enterprise-grade software. Tax domain knowledge is not required. The successful candidate should be comfortable learning business workflows from subject matter experts and translating those workflows into reliable software systems. You will own technical direction across backend services, asynchronous processing, AI integrations, cloud infrastructure, data persistence, security, and observability, balancing speed, quality, risk, and maintainability. What You’ll Do - Architecture & Technical Leadership: Lead the architecture, design, and delivery of AI-enabled software products and internal platforms, owning decisions across backend services, async processing, AI integrations, cloud, data, and security. - Team Leadership & Mentorship: Manage and mentor software engineers, quality assurance professionals, and business analysts, providing technical guidance, code review, delivery planning, and career development. - Cloud & Platform Engineering: Own cloud architecture on Microsoft Azure, including async and event-driven workloads, background jobs, and reliable, cost-effective operations. - Security & Data Protection: Ensure secure handling of data, secrets, authentication, authorization, tenant isolation, encrypted storage, and auditability. - Delivery & Engineering Excellence: Improve engineering practices around testing, CI/CD, monitoring, deployment, documentation, and operational support. - Architecture & Delivery: Establish development standards for clean architecture, dependency injection, async design, and API consistency. Maintain and evolve a Python/FastAPI monorepo with multiple application services and shared packages. Make pragmatic architecture decisions that balance speed, quality, risk, and long-term maintainability. - AI Integration & Document Processing: Guide integrations with Azure Foundry, Azure AI Document Intelligence, and other Azure resources across document-processing and AI-assisted analysis pipelines. Establish responsible, repeatable patterns for prompting, embeddings, semantic search, and AI evaluation within controlled enterprise tools. - Cloud, Async & Operations: Oversee asynchronous job processing using Azure Service Bus and Azure Functions. Support production troubleshooting, performance tuning, cost management, and reliability improvements. Strengthen observability through structured logging, tracing, and Azure Monitor / Application Insights. - Security & Compliance: Ensure secure handling of data, secrets, identity, authorization, tenant isolation, encrypted storage, and audit logging. Partner with security and compliance stakeholders on identity, access, and responsible-use standards. Stakeholders & Collaboration Model - Development team (direct reports, delivery owners) - Product owners and business/tax SMEs (requirements, prioritization, workflows) - Security, compliance, and infrastructure teams (identity, governance, approved tooling) - Client-facing and leadership stakeholders (roadmap, delivery commitments, business outcomes) Technology Environment - Core Backend & Data: Python 3.12+, FastAPI, and async-first service design (AsyncIO), Pydantic, Uvicorn, SQLAlchemy (async ORM) with Alembic migrations, Azure PostgreSQL. - Cloud & Platform (Microsoft Azure): Azure App Service and Azure Functions, Azure Service Bus for async, queue-based processing, Azure Blob Storage and Azure Key Vault, Azure API Management and Managed Identity, Azure Monitor / Application Insights. - AI & Document Processing: Azure Foundry and the OpenAI Python SDK (chat and embeddings), Microsoft Agentic Framework; Azure AI Document Intelligence, FAISS, scikit-learn, and RapidFuzz for semantic search and matching, PyMuPDF, pandas, and numpy for document and data processing. - Security & Identity: Microsoft Entra ID / Azure AD and Okta, OAuth2 / OIDC, JWT, and machine-to-machine API authentication, Key Vault-backed secrets, encrypted storage, tenant isolation, and audit logging. - DevOps, Testing & Observability: Azure DevOps Pipelines and Octopus Deploy; Veracode scanning, pytest, pytest-asyncio, and pytest-cov, OpenTelemetry and structlog (structured logging). - Frontend / UI (lightweight): Jinja2 templates, HTML/CSS, and vanilla JavaScript served via FastAPI. Qualifications - 7+ years of professional software engineering experience. - 2+ years leading engineers as an engineering manager, technical lead, staff engineer, or architecture lead. - Strong hands-on Python backend development experience, including production web APIs and distributed systems. - Strong understanding of cloud architecture, preferably Microsoft Azure. - Experience with async processing, queue-based workloads, background jobs, and event-driven systems. - Experience with relational databases—preferably PostgreSQL—including SQLAlchemy and schema migrations. - Familiarity with CI/CD, automated testing, code review, release management, and production support. - Strong security mindset across secrets management, identity, authorization, encryption, and auditability. - Ability to communicate clearly with both technical and non-technical stakeholders and to make pragmatic architecture trade-offs. - Bachelor's degree required. Preferred Qualifications - Experience building AI-enabled or LLM-powered applications (Azure OpenAI, OpenAI APIs, prompt engineering, embeddings, semantic search, or AI evaluation). - Experience with document extraction, OCR, PDF processing, or Azure AI Document Intelligence. - Experience with multi-tenant SaaS or enterprise internal platforms. - Experience with Microsoft Entra ID, Okta, OAuth2/OIDC, JWT, and API gateway patterns. - Experience with Azure API Management, Azure Monitor, Application Insights, and Managed Identity. - Experience modernizing legacy applications or moving teams toward cleaner architecture and stronger engineering practices. - Experience with financial, accounting, tax, compliance, or professional services workflows is helpful but not required. Benefits - Your exceptional people experience starts here. At Crowe, we know that great people are what makes a great firm. We care about our people and offer employees a comprehensive total rewards package. - We will nurture your talent in an inclusive culture that values diversity. You will have the chance to meet on a consistent basis with your Career Coach that will guide you in your career goals and aspirations.

Role Description Deephaven Mortgage is seeking a Lock Desk-Scenario Desk intern to join us for a paid internship program! We are seeking a recent college graduate to work with our Capital Markets & Underwriting Team in Charlotte, NC and remote. The candidate will get firsthand training and education related to the various facets of Lock Desk and Scenario Desk within the Financial and Mortgage Lending verticals. There will be an opportunity to learn and participate in ongoing analytical and operational projects. Qualifications - Strong critical thinking and problem-solving skills are essential - Self-driven and motivated individual with a collaborative mindset to introduce innovative ideas - Strong interpersonal skills and a commitment to maintaining a high standard of professionalism in all interactions - Proficiency in Microsoft Programs: Excel, Teams, Outlook Benefits - Flexible Time to Recharge – Personalized Time Off for What Matters (because rest isn't one-size fits all) - Future Finance Focused – Generous employer-matched 401(k) plan - Community Connect – Philanthropy Committee that creates charitable initiatives - Health from Day One – Comprehensive health insurance starting on day one of employment - Family Matters – Competitive maternity and paternity leave - Culture & Celebrations – Culture Committee with team-building events and celebrations - Ideas Welcome – Encouraging thought leadership and innovation - Guidance & Growth – Mentorship opportunities for career development - Perk Up – Exclusive discounts on travel, tech, pets, legal, and more

Role Description Ziply Fiber is seeking a Director of Artificial Intelligence (AI) to lead the company’s AI vision, roadmap, and execution with a strong emphasis on hands-on delivery and measurable business outcomes. This leader will build and scale AI capabilities that improve customer experience, streamline operations, and enhance network performance across the enterprise. This role is ideal for a highly technical and pragmatic leader who can personally engage in solution design, experimentation, vendor evaluation, and implementation while also leading cross-functional teams. The Director will focus on reducing churn, lowering customer complaints, reducing avoidable dispatches, and delivering automation across Customer Care, Field Services, Dispatch Centers, IVR, Digital Channels, and Network Automation. Success in this role requires balancing innovation, speed, governance, and business value while establishing AI as a strategic differentiator for Ziply Fiber. The Director will help move the organization from isolated pilots to scalable, production-grade AI solutions that deliver tangible impact for customers, employees, and network operations. Key Success Metrics - Deployment and adoption of high-impact AI use cases across customer care, field services, dispatch, digital channels, IVR, and network operations. - Measurable reductions in churn, customer complaints, repeat contacts, and unnecessary dispatches. - Improvements in customer experience, operational efficiency, first-contact resolution, and network performance. - Establishment of a scalable, governed AI ecosystem that enables sustained innovation and business value. Essential Duties and Responsibilities - AI Strategy & Leadership - Define and lead Ziply Fiber’s enterprise AI strategy and roadmap, aligned to business priorities and digital transformation goals. - Establish a centralized AI operating model that reduces fragmentation and accelerates enterprise-wide adoption. - Partner with executive leadership to identify, prioritize, and sequence high-impact AI use cases with measurable business outcomes. - Act as a hands-on leader who can move from strategy to prototype to production with urgency and discipline. - Solution Development & Execution - Lead the design, development, and deployment of scalable AI and machine learning solutions across customer care, field services, dispatch centers, IVR, digital channels, and network operations. - Develop AI-driven capabilities that reduce churn, lower complaint volumes, improve first-contact resolution, and reduce avoidable truck rolls and dispatches. - Drive automation use cases such as agent assist, intelligent routing, conversational AI, digital self-service, predictive dispatching, workforce recommendations, and network event correlation. - Own end-to-end execution from ideation through production deployment, performance monitoring, and value realization. - Data & Platform Enablement - Collaborate with data, IT, engineering, and operations teams to build robust AI and ML platforms, data pipelines, and reusable services. - Ensure the availability, quality, and governance of data required to support AI use cases across customer, operational, and network domains. - Champion modern AI tooling, MLOps practices, observability, and engineering standards that enable scalable and reliable deployment. - Remain hands-on with architecture, experimentation, model evaluation, vendor selection, and technical trade-off decisions. - Governance & Responsible AI - Establish and enforce AI governance frameworks that ensure compliance, security, privacy, and ethical use of AI solutions. - Define standards for model lifecycle management, explainability, monitoring, and accountability. - Mitigate risks related to bias, hallucinations, data quality, cybersecurity, and regulatory requirements. - Ensure business processes, controls, and adoption plans are designed for sustainable production use rather than isolated pilots. - Cross-Functional Collaboration - Work closely with customer care, field services, dispatch, network operations, product, finance, and IT teams to identify friction points and deploy AI solutions that improve outcomes. - Serve as a thought leader who raises AI literacy, builds trust, and accelerates adoption across the organization. - Translate business needs into technical execution plans and ensure initiatives remain aligned to measurable value and business priorities. - Team Leadership & Capability Building - Build and lead a high-performing team of data scientists, ML engineers, AI engineers, and analysts. - Develop internal AI capabilities while strategically leveraging external partners, vendors, and platform providers. - Create a culture of experimentation, accountability, rapid learning, and continuous improvement. - Coach teams to move quickly while maintaining strong engineering discipline, governance, and operational supportability. - Documentation, Standards & Best Practices - Own and evolve technical standards, operational procedures, diagrams, and design documentation. - Ensure high quality Ops guides, MOPs, and runbooks are produced and maintained. - Conduct technical research and stay current on emerging technologies and industry best practices. - Other Duties - Performs other duties as required to support the business and evolving organization. Qualifications - Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field. - Minimum of ten (10) years of experience in AI architecture, data science, or software engineering, including large-scale production deployments of machine learning, deep learning, or AI-driven systems in enterprise environments, with five (5)+ years in leadership roles. - Demonstrated ability to design cloud-native and edge AI architectures, integrating models, APIs, and agents into enterprise platforms. - Proficiency with multi-agent orchestration using Model Context Protocol (MCP), Agent-to-Agent (A2A) interaction models, retrieval-augmented generation (RAG), vector databases, and context memory architectures. - Proven experience designing and implementing enterprise-grade AI platforms leveraging both classic machine learning techniques such as forecasting, optimization, and predictive modeling, and modern generative and agentic frameworks. - Proficiency with cloud-scale AI ecosystems such as Microsoft Copilot, Azure AI, OpenAI, AWS, or Google Cloud, and strong familiarity with modern data governance platforms. - Proficiency in Python and familiarity with SQL, R, or Java. - Hands-on experience with frameworks such as TensorFlow, scikit-learn, and Hugging Face, as well as workflow orchestration or MLOps tools such as MLflow, Kubeflow, and Airflow. - Experience implementing governance, monitoring, and Responsible AI practices that ensure safety, transparency, reliability, and production readiness. Preferred Qualifications - Master’s degree in related field. - Experience in telecommunications, network operations, customer care, or digital service organizations. - Familiarity with use cases such as churn prediction, complaint reduction, dispatch optimization, IVR automation, conversational AI, digital self-service, and network event intelligence. - Experience with cloud-based AI platforms such as Azure AI, AWS, or Google Cloud. - Strong executive communication, stakeholder management, and vendor evaluation skills. Knowledge, Skills, and Abilities - Ability to work independently and apply sound judgment and reasoning skills to a variety of situations. - Ability to multi-task and collaborate effectively with other personnel to meet deadlines. - Strong verbal and written communication, attention to detail, and organizational skills. Benefits - Medical, dental, vision, 401k, flexible spending account, paid sick leave and paid time off, parental leave, quarterly performance bonus, training, career growth and education reimbursement programs.

Role Description The AI Security and Governance Architect plays a critical role in safeguarding the company’s software, AI, and machine learning (ML) systems by ensuring secure design, ethical governance, and proactive risk management across the AI lifecycle. This position leads efforts to align AI development and deployment with both cybersecurity and regulatory frameworks, fostering a culture of responsible AI use and compliance. You will collaborate closely with product, data science, compliance, and legal teams to establish standards for AI security, model governance, and ethical use of emerging technologies. Your Role Responsibilities — Here's What You'll Do - AI Security & Governance - Define and implement the organization’s AI security and governance framework, incorporating best practices from NIST AI RMF, ISO/IEC 23894, and relevant regional regulations such as the EU AI Act. - Develop and enforce AI governance policies that align with internal risk appetite, ethical standards, and external regulatory obligations. - Identify, assess, and mitigate AI-specific risks including model bias, adversarial attacks, data leakage, model inversion, and poisoning. - Establish secure ML lifecycle practices including data sanitization, model hardening, explainability, and continuous monitoring for model drift and degradation. - Collaborate with compliance and legal partners to maintain awareness of AI-related laws and standards (e.g., GDPR, Algorithmic Accountability Act). - Security Architecture & Risk Management - Maintain and evolve threat models for AI and software systems, ensuring they remain current, comprehensive, and risk-informed. - Lead reviews of software and model architectures to ensure compliance with established security and AI governance standards. - Regularly assess and prioritize remediation of vulnerabilities in both internally developed and third-party AI components. - Ensure security verification tools and AI audit mechanisms are effective, consistent, and automated where possible. - Governance, Monitoring, and Incident Response - Design and oversee AI auditing and monitoring programs to ensure ethical and secure use of AI models post-deployment. - Develop AI-specific incident response playbooks and lead investigations into security or ethics-related AI failures. - Implement governance tooling and automation, leveraging ModelOps or compliance platforms (e.g., MLflow, OpenScale, Credo AI) to streamline audit logging and compliance checks. - Education, Culture, and Leadership - Define and deliver education programs for engineers, data scientists, and testers on secure AI and software development practices. - Foster a culture of ethical and responsible AI innovation, ensuring teams balance speed with security and compliance. - Mentor and coach peers, providing feedback on best practices for AI governance and security-by-design. - Act as a trusted advisor to leadership and cross-functional stakeholders on AI trends, risks, and emerging regulatory impacts. Qualifications - Bachelor’s degree in Computer Science, Data Science, Information Security, or a related field. - Minimum 8 years of progressive security architecture experience, with at least 3+ years in AI/ML system security, governance, or compliance. - Strong understanding of secure development practices, cloud security (AWS/Azure/GCP), and compliance frameworks such as SOC 2, GDPR, and NIST. - Hands-on experience with AI/ML pipelines, including data preparation, model deployment, and monitoring. - Experience with SAST, DAST, SCA, and AI governance tools. - Familiarity with AI ethics, fairness, and bias mitigation techniques. - Demonstrated ability to translate technical AI risks into business language for non-technical stakeholders. Requirements - Experience implementing AI risk management frameworks (NIST AI RMF, ISO/IEC 23894, EU AI Act classifications). - Scripting and automation skills (e.g., Python, PowerShell, or Java). - Experience with policy development and cross-functional AI governance committees. - Certifications such as CCSP, CISSP, or AI Governance/Responsible AI credentials. Core Competencies - Strong leadership, communication, and influence skills across technical and non-technical teams. - Deep understanding of threat modeling, compliance integration, and risk assessment. - Demonstrated ability to handle sensitive and ethical issues with discretion and integrity. - Excellent organizational, analytical, and critical-thinking skills with the ability to manage multiple priorities. - Self-motivated, proactive, and capable of driving programs independently.