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About This Role
Job Title: Principal Solution Architect
Department: Solution Engineering
About AppViewX:
AppViewX is the only machine and agent identity security company built for the AI and quantum era, bringing together discovery, automation, control, and intelligence. The AVX Platform helps enterprises reduce risk by providing complete visibility and governance over every machine and AI agent identity, automating their lifecycle, controlling their access, and proving compliance at the speed business demands. Trusted by global enterprises across financial services, healthcare, and technology, AppViewX is recognized as a leader in the IDC 2026 MarketScape for Certificate Lifecycle Management and KuppingerCole's 2025 Non\-Human Identity Management Leadership Compass. For more information, users can visit appviewx.com.
Our Values
At AppViewX, our values reflect how we work together in practice—not just what we aspire to. They show up in everyday decisions, how we collaborate across teams, and how we treat each other while building and delivering our work. If these values resonate with you, you'll likely feel at home here.
- Clarity: We interact with transparency, simplicity, and shared purpose.
- Unity: We build unity through mutual respect, trust, and collaboration.
- Innovation: We stay curious, challenge assumptions, and drive continuous improvement.
- Speed: We act with urgency, focus, and follow\-through to deliver results fast.
- Precision: We bring accuracy, consistency, and care to everything we do.
Role Overview:
The Principal Architect (Agentic Identity) is a newly created, specialized role within the Global Solutions Engineering team, chartered to build and own AppViewX's agentic identity security (AIS) category leadership in the field. This is a hybrid role by design: half strategic evangelist, half hands\-on architect.
As Principal Architect, this person is AppViewX's most credible external voice on agentic identity \- engaging CISOs and CTOs at strategic accounts, shaping the AIS narrative with analysts (Gartner, KuppingerCole, Forrester), and representing the company at industry events. As Principal Architect, this person personally owns the technical backbone of AIS pre\-sales: the Use Case Map, demo environments, POC frameworks, and reference architectures that the rest of the global SE team (AMS, EMEA, APAC) builds on.
The role reports to the VP of Global Solutions Engineering and works in close partnership with SE Leads and product stakeholders to bring AIS to sell\-ready maturity and beyond.
Key Responsibilities:
Strategic Field Leadership \& Evangelism
- Serve as AppViewX's foremost technical voice on agentic identity security, in the field and in public \- conferences, webinars, analyst briefings, and published content.
- Build and maintain analyst relationships (Gartner, KuppingerCole, Forrester) to shape how AIS is categorized and evaluated in the market.
- Engage CISO\- and CTO\-level stakeholders directly on the most strategic AIS opportunities, acting as an executive\-level trusted advisor alongside regional SE leads and AEs.
- Partner with Sales and GTM leadership to shape Go to market and revenue acceleration strategy.
- Partner with Product Marketing and Product Management to shape AIS positioning, messaging, and roadmap priorities informed directly by field signal.
Technical Architecture \& Enablement Ownership
- Own and continuously evolve the AIS Use Case Map, Demo Runbook, and reference architectures used across the global SE organization.
- Personally design and build flagship AIS demo environments and POC/POV frameworks, including integration patterns and success\-criteria templates.
- Partner directly with AIS product engineering to translate roadmap into sellable, demoable capability, and drive AIS pre\-sales enablement to sell\-ready status and beyond.
- Maintain deep, current hands\-on fluency with the AIS platform, including underlying agentic AI, non\-human identity, and workload identity governance concepts.
Enterprise Pre\-Sales \& POC Execution
- Lead or co\-lead the most strategic and complex AIS opportunities globally, engaging directly alongside AMS, EMEA, and APAC SE teams.
- Architect and execute high\-stakes AIS POCs/POVs, including technical prerequisites, integration scoping, success\-criteria definition, and executive readout.
- Act as the final technical escalation point for AIS deals across regions, ensuring consistent, credible technical narrative in front of customers.
Team Enablement \& Capability Building
- Design and deliver AIS training curriculum and certification paths for the global SE team.
- Build battlecards, FAQs, and RFP response libraries specific to agentic identity, keeping the team current against an emerging and fast\-moving competitive landscape.
- Mentor SE Leads and SEs on agentic identity concepts, positioning, and objection handling.
Product \& Market Feedback Loop
- Serve as a structured, credible voice of the field back to Product Management on AIS roadmap priorities, feature gaps, and competitive intelligence.
- Track and analyze the emerging agentic identity competitive landscape, including both new entrants and IAM incumbents extending into the space.
Required Qualifications
- 10\+ years of experience in enterprise security pre\-sales, solutions architecture, or a senior technical leadership role, with direct exposure to identity, IAM, machine identity, or related security domains.
- Strong working knowledge of agentic AI systems, non\-human/workload identity, and service account governance \- and the ability to speak credibly to both engineering and executive audiences.
- Demonstrated track record as an external\-facing technical evangelist: conference speaking, analyst engagement, or published thought leadership.
- Proven hands\-on architecture and build capability \- this is not a strategy\-only role; the person must be able to personally build and demo complex technical environments.
- Demonstrated experience engaging CISO\- and CTO\-level stakeholders on strategic, multi\-stakeholder deals.
- Strong written and verbal communication skills, able to move fluidly between deep technical detail and executive narrative.
- Bachelor's degree in Computer Science, Information Systems, Cybersecurity, or equivalent experience.
- Willingness and ability to travel 30–50% for customer, analyst, and industry engagements.
Preferred Qualifications
- Prior title or scope equivalent to Field CTO, Distinguished Engineer, or Principal Architect.
- Experience with identity governance platforms, OAuth/OIDC, IAG tools, or broader user/ machine identity ecosystems.
- Existing relationships with analyst firms (Gartner, KuppingerCole, Forrester) covering identity or AI security.
- Experience integrating or go\-to\-market planning around an acquired product line (M\&A\-driven category building).
- Published thought leadership: blogs, whitepapers, podcasts, or conference talks on identity security or AI\-driven risk.
- Familiarity with the broader AppViewX platform: PKI, CLM, SSH, Kubernetes TLS automation, Code Signing, and PQC (ML\-KEM/ML\-DSA, FIPS 203/204/205\).
Why AppViewX?
AppViewX caters to a wide range of customers from Fortune 1000 companies, including six of the top ten global commercial banks, five of the top ten global media companies, and five of the top ten managed healthcare providers. Over the years, we grew our diverse team, perfected our automation platform, and expanded our global footprint to India, North America, United Kingdom, and Canada.
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 AppViewX, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
AppViewX AI Hiring
AppViewX has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% above the national median.
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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