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About This Role
Job Description
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Position Overview
Reporting to the SVP, Software Engineering and AI Operations, the Director of AI Governance \& Enterprise Solutions is Momentive's senior AI subject matter expert — the person who sets the technical bar for how the company designs, builds, deploys, and governs AI agents. This role owns the Momentive Context Library, defines and enforces the standards that all AI agents must meet, leads the review and approval of agents across the portfolio, and directly builds agents and skills for the AI Operations Hub. When business units need to build AI solutions, this leader will be a strategic technical partner.
Core Responsibilities
Context Library Ownership
- Own the Momentive Context Library — the shared knowledge repository that AI agents across the company depend on for accurate, consistent, organization\-specific context.
- Define the library's architecture, taxonomy, and content standards, ensuring it is well\-organized, current, and designed to be consumed reliably by agents across different use cases.
- Define and maintain the standards for centrally\-owned content — shared company knowledge, cross\-functional reference material, and foundational context that spans business units — ensuring it remains accurate, current, and consistently structured.
- Partner with AI Operations Hub spoke leads to help them build, structure, and maintain department\-owned content — including converting existing documentation and institutional knowledge into library\-ready context.
Agent Review \& Standards
- Define the technical review criteria and quality standards that all AI agents, skills, and connectors must meet for AI Operations Hub approval — covering design, architecture, data handling, accuracy, safety, and operational readiness.
- Lead the technical review and approval process for all AI agents submitted through the AI Operations Hub intake process, making or formally endorsing go/no\-go recommendations.
- Develop agent development guidelines, design patterns, and templates that translate AI Operations Hub standards into practical, reusable building blocks for spoke teams.
- Identify systemic quality issues, common failure modes, and emerging risks across the agent portfolio, and use those insights to continuously raise the technical bar.
Building for AI Operations
- Directly design and build AI agents, skills, and connectors for the AI Operations team's own use — including tooling that supports AI Operations Hub functions like intake review, registry management, and performance monitoring.
- Prototype and evaluate new agent architectures, orchestration frameworks, and tooling approaches before recommending them for broader organizational use.
Technical Consulting \& Deployment Guidance
- Serve as the primary technical advisor to spoke teams as they design and build AI solutions — providing hands\-on architecture guidance, reviewing approach before build begins, and troubleshooting when things go wrong.
- Own the AI Operations Hub's standards for how agents are deployed, monitored, and secured in production — including environment configuration, observability requirements, access controls, and incident response protocols.
- Partner with Corporate IT on the platform and infrastructure decisions that affect how agents are deployed and maintained across the organization.
- Assess and recommend AI platforms, tools, and vendor solutions as the technology landscape evolves, with a focus on what will actually serve Momentive's operational model.
- Partner on how AI agents and automation intersect with the enterprise application portfolio — aligning platform strategy, tooling investments, and governance standards across both functions.
Enablement
- Develop technical reference materials, design pattern libraries, and decision guides that help spoke teams make better architectural choices independently.
- Deliver targeted technical training and workshops when spoke teams need to build a foundational capability — this is a supporting function, not a primary one.
- Actively connect spoke teams to each other — surfacing shared use cases, reusable patterns, and prior work across business units so teams build on each other's progress instead of solving the same problems independently.
- Other duties as assigned.
Qualifications
- Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent work experience.
- 7\+ years of hands\-on experience designing and building software or AI systems, with at least 3 years working directly with large language models, AI agents, and production AI deployments.
- Deep practical expertise with AI agent architectures, LLM APIs, orchestration frameworks (LangChain, LlamaIndex, CrewAI, AutoGen, or similar), and the tradeoffs between them.
- Expert\-level knowledge of prompt engineering, retrieval\-augmented generation (RAG), tool use and function calling, context window management, and agent evaluation methodologies.
- Demonstrated experience building and deploying AI agents in production environments — including decisions around deployment architecture, observability, access controls, and failure handling.
- Experience designing or governing shared knowledge repositories, context management systems, or information architectures for AI systems.
- Strong instincts for AI risk — understanding where agents fail, how to test for it, and how to build systems that degrade gracefully.
- Experience with enterprise AI platforms such as Claude Enterprise, Azure OpenAI Service, Google Vertex AI, or similar.
- Strong written and verbal communication skills — able to make complex technical positions clear and defensible to both engineering and non\-technical leadership audiences.
- Familiarity with AI governance frameworks (NIST AI RMF or similar) and responsible AI principles as they apply to production systems.
- Experience operating in a cross\-functional environment where you set standards others must follow, not just your own team.
- Demonstrated growth mindset and commitment to staying ahead in a rapidly evolving field.
What Success Looks Like
- Every AI agent that reaches production has been reviewed against a clear, defensible technical standard — and the review process makes agents better, not just slower.
- The Momentive Context Library is the most trusted source of organizational knowledge available to AI agents — current, well\-structured, and actively relied upon by spoke teams.
- AI Operations' own agents are high\-quality, well\-maintained, and set the standard that spoke teams aspire to.
- Spoke teams leave technical consultations with a clearer path forward and better architectural decisions than they would have made on their own.
- Production agents are deployed consistently, monitored reliably, and secured according to a clear, documented standard.
- The AI Operation’s Hub technical bar is high and rising — and the organization's AI portfolio reflects it.
- New capabilities are evaluated and piloted by the AI Operations Hub before spoke teams start asking for them.
About Us
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Momentive Software amplifies the impact of over 20,000 purpose\-driven organizations in over 30 countries, with over $11 billion raised and 55 million members served to date. Mission\-driven nonprofits and associations rely on Momentive’s cloud\-based software and services to address their most pressing challenges – from engaging their communities to simplifying operations and growing revenue. Designed to help organizations connect more, manage more, and ultimately expect more, Momentive's solutions are built with reliability at the core and strategically focus on fundraising, learning, events, careers, volunteering, accounting, and association management. Momentive partners with organizations that believe "good enough" is never enough – so they can bring on better outcomes for everyone they serve. Learn more at momentivesoftware.com .
Why Work Here?
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At Momentive Software, we’re a team of passionate problem\-solvers, innovators, and volunteers who believe in using technology to make a real difference. We dream big, support each other, and take pride in creating solutions that help our customers drive meaningful change. If you’re looking for a place where your work matters and your ideas are valued, you’ll find it here.
Medical, Dental \& Vision Benefits
401(k) Savings Plan with Company Match
Flexible Planned Paid Time Off
Generous Sick Leave
Inclusive \& Welcoming Environment
Purpose\-Driven Culture
Work\-Life Balance
Commitment to Community Involvement
Employer\-Paid Parental Leave
Employer\-Paid Short\-Term Disability
Remote Work Flexibility
Momentive Software actively embraces diversity and equal opportunity in a meaningful way. We are committed to building a team that represents a variety of backgrounds, perspectives, and skills. The more inclusive we are, the better our work will be, which is why we do not discriminate based on race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, veteran status, or any other status protected under federal, state, or local law.
All persons hired will be required to verify identity, minimum age of 18, eligibility to work in the United States (without sponsorship), and to complete the required employment eligibility verification form upon hire.
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 Momentive Software, 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.
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.
Momentive Software AI Hiring
Momentive Software has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
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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