Vice President Software Engineering (AI)

Location: Dallas, TX, United States

Company

hirify.global develops and operates engineering, data, analytics, and quantitative platforms supporting risk, reporting, compliance, treasury, human capital, and other core financial functions.

What you will do

• Design, build, deploy, and operationalize cloud-native AI applications using modern engineering practices, CI/CD, and automated testing.
• Partner with business and engineering stakeholders to identify high-impact AI opportunities and translate requirements into scalable architectures, data models, and technical specifications.
• Embed with business teams to map workflows, identify automation opportunities, define human approval and control boundaries, run pilots, and measure adoption.
• Integrate LLM APIs, retrieval-augmented generation, embeddings, vector search, prompt and context management, structured outputs, validation, and evaluation harnesses.
• Apply cloud, DevOps, MLOps, observability, secure deployment, lifecycle management, and cost and resilience practices.
• Document solutions, mentor receiving teams, and transition application code, integrations, data models, and operational practices into long-term ownership.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related quantitative field.
• 9+ years of hands-on experience building, deploying, and supporting production software applications.
• Strong proficiency in Python, Java, or Go, with sound software engineering, testing, data modeling, and system design skills.
• Experience translating complex business requirements into cloud-optimized architectures and scalable production solutions.
• Extensive experience with AWS, Azure, or GCP, including serverless services, containers, managed services, automated deployment, monitoring, and cloud security.
• Production experience integrating AI/ML capabilities, including RAG pipelines, embeddings, vector databases or search indexes, structured outputs, response validation, evaluation datasets, and model monitoring.

Nice to have

• Experience building agentic AI systems with multi-step planning, tool and API calls, state management, permission checks, failure handling, and auditable action trails.
• Experience implementing agent observability, human-in-the-loop approvals, policy checks, incident handoff, and post-action summaries.
• Experience optimizing production AI systems through model routing, retrieval tuning, caching, batching, streaming, asynchronous execution, cost controls, and regression testing.

Culture & Benefits

• Work within a centralized engineering structure supporting consistent governance and scalable solutions.
• Collaborate with cross-functional business and engineering teams in a forward-deployed internal client-facing model.
• Enable receiving teams through documentation, mentoring, and structured knowledge transfer.