Technology And Data - Software Engineer 4 - Contingent

Software Engineering Consultant

In this contingent resource assignment, you may: Consult on complex initiatives with broad impact and large-scale planning for Software Engineering. Review and analyze complex multi-faceted, larger scale or longer-term Software Engineering challenges that require in-depth evaluation of multiple factors including intangibles or unprecedented factors. Contribute to the resolution of complex and multi-faceted situations requiring solid understanding of the function, policies, procedures, and compliance requirements that meet deliverables. Strategically collaborate and consult with client personnel.

Required Qualifications: 5+ years of Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work or consulting experience, training, military experience, education.

Partner with business stakeholders, product owners, and engineering teams to understand business problems, identify AI-enabled opportunities, and define end-to-end technical solutions. Design, prototype, and deliver AI-driven workflows, agents, copilots, and automations using large language models (LLMs) and enterprise AI services. Integrate AI capabilities with enterprise platforms and systems (e.g., ServiceNow, Salesforce, data platforms, internal services) using secure APIs and orchestration patterns. Rapidly iterate on prototypes and transition them into production-ready solutions that meet enterprise standards for reliability, scalability, and supportability. Act as a technical bridge between business, product, data, security, and engineering teams to ensure solutions are usable, compliant, and aligned with business objectives. Lead solution architecture and design activities, including: prompt engineering and AI workflow design, API integration and service orchestration, enterprise knowledge and data integration, security, privacy, risk, and governance considerations. Own solutions across the full lifecycle—from concept and proof of value through production deployment and continuous improvement. Apply and promote best practices for responsible AI, including model risk management, data protection, and compliance with enterprise and regulatory requirements. Contribute to the development of reusable patterns, standards, and guidance to support scalable AI adoption across the enterprise.

EEO: "Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of – Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans."