Associate Director, AI Ops Engineering

Vista Equity Partners Management•Remote,

5d•$215,000 - $260,000

About The Position

AI Ops Engineering is the platform engineering team within the Value Creation Team (VCT), the group that partners with Vista companies to accelerate growth and operational performance. Our mandate is to industrialize value creation with agentic AI: we take AI capabilities proven in the field and engineer them into a shared, production grade foundation that serves engagements across the portfolio. We build and operate the systems that turn promising ideas into dependable, reusable products, so that operational intelligence built once strengthens every engagement and every company it touches. This is a hands-on engineering team for people who ship to production, work across functions, and care about building AI systems that are reliable, governed, and built to scale. As an Associate Director, AI Ops Engineering, you will design, build, and operate the agentic AI platform and the applications that run on it. You will own hands on implementation across the full stack, from agent architecture and orchestration through data, infrastructure, and the interfaces that make capabilities usable in production. You will also help bring AI tools built across the practices onto a common foundation, and partner closely with product, engineering, and cross-functional stakeholders to move capabilities from concept to durable, production grade solutions. This role is for an immediate start.

Requirements

Generative AI, including large language models (LLMs), inference, retrieval-augmented generation (RAG) architectures, and diffusion models
Agentic architectures, autonomous agents, and multi-agent systems
Front-end development and user interface and user experience (UI/UX) design patterns for complex, data-rich, and AI-native applications
Familiarity with modern design and prototyping tools and AI-assisted design workflows
Cloud AI platforms across Amazon Web Services (AWS), Microsoft Azure, and Google Cloud (GCP)
AI model development, deployment, and lifecycle management
Prompt engineering and model fine-tuning
Machine learning and deep learning fundamentals
Platform abstraction and vendor-independent architecture design
Shipping AI applications to production environments, and operating them for reliability, observability, and cost
Rapid proof-of-value delivery and compressed development timelines
Cross-organizational collaboration and stakeholder management across multiple companies simultaneously
AI maturity assessment and improvement planning
Translating complex technical concepts for executive audiences
Problem-solving and analytical thinking in ambiguous environments
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or a related field
3+ years of experience in AI/ML platform architecture and development, with deep recent experience (2+ years) in generative AI and agentic architectures in production applications
Demonstrated track record of shipping AI applications to production environments, not just prototypes
Strong understanding of public cloud AI services and the ability to architect vendor-agnostic solutions with appropriate abstraction layers
Excellent communication, presentation, and interpersonal skills

Nice To Haves

Depth in front-end engineering and UI/UX design, including hands-on experience shipping polished, user-facing interfaces, is a strong plus for this role
Experience within private equity portfolio companies, consulting, or multi-client environments is a strong plus

Responsibilities

Design, build, and iterate on agentic AI systems, moving from validated concept to production grade capability with clear stage gates and measurable outcomes.
Design and build intuitive, accessible interfaces for agentic AI products, partnering with solution owners to translate concepts and prototypes into production-ready experiences.
Own hands-on implementation of agent architectures (single-agent, multi-agent, human-in-the-loop) across a range of enterprise use cases, and build the production stack that supports them, including interfaces, APIs and data integrations, identity and access, data and memory, infrastructure, and observability.
Develop and stress-test reference implementations for core agentic capabilities, including tool use, memory, planning, orchestration, and inter-agent communication, to establish what works reliably at enterprise scale.
Design abstraction layers and reusable infrastructure components that preserve vendor independence across large language model (LLM) providers, orchestration frameworks, and cloud environments.
Help onboard and enable AI tools built across the practices onto a shared platform, so that capabilities run on common infrastructure, standards, and governance.
Define and evolve technical standards for agent development: model selection, prompt architecture, autonomy calibration, observability, and guardrails, ensuring every capability is built on a foundation that can survive production.
Establish evaluation frameworks and scoring rubrics for agent performance, reliability, and safety that can be operationalized consistently across the platform.
Partner across product, engineering, and cross-functional stakeholders, translating validated field patterns into reusable platform capabilities and managing stakeholders across multiple teams and companies simultaneously.
Continuously assess the frontier of agentic AI, including new models, frameworks, tooling, and architectural patterns, and translate findings into concrete recommendations for what to build, buy, or watch.
Serve as a practitioner voice on generative AI and agentic systems, contributing to Vista's thought leadership and helping shape the platform's technical direction in partnership with leadership.