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About This Role
Position Summary:
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.
Responsibilities:
Platform Engineering and Development
- 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.
Platform Standards, Onboarding, and Enablement
- 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.
Collaboration and Emerging Technology Evaluation
- 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.
- The annualized base pay range for this role is expected to be between $215,000 \- $260,000\. Actual base pay could vary based on factors including but not limited to experience, subject matter expertise and the applicant's skill set. The base pay is just one component of the total compensation package for employees. Other rewards may include an annual cash bonus and a comprehensive benefits package.
Qualifications:
Technical Expertise
- 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
Operational Expertise
- 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
Qualifications
- 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
- 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
- Excellent communication, presentation, and interpersonal skills
- Experience within private equity portfolio companies, consulting, or multi\-client environments is a strong plus
Company Overview:
Vista is a leading global investment firm that invests exclusively in enterprise software, data and technology\-enabled organizations across private equity, credit, public equity and permanent capital strategies. The firm brings an approach that prioritizes creating enduring market value for the benefit of its global ecosystem of investors, companies, customers, and employees. Vista's investments are anchored by a sizable long\-term capital base, experience in structuring technology\-oriented transactions and proven, flexible management techniques that drive sustainable growth. Vista believes the transformative power of technology is the key to an even better future, a healthier planet, a smarter economy, a diverse and inclusive community, and a broader path to prosperity.
Salary Context
This $215K-$260K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Vista Equity Partners Management, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($237K) sits 11% above the category median. Disclosed range: $215K to $260K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Vista Equity Partners Management AI Hiring
Vista Equity Partners Management has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $260K - $260K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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