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About This Role
Remote
Full Time
Intermediate or Experienced
Bradenton, Florida, United States
About the job
=================
CoAdvantage is an HCM company providing payroll, ASO, and PEO services to 16,000 clients. We deliver payroll, benefits, HR compliance, time/PTO, and risk management solutions, and we are building a governed AI platform that will become a primary source of differentiation versus AI\-native competitors.
We are standing up a parallel AI architecture\- a microservice plane that talks to legacy systems via ETL contracts\- anchored on three substrates (engineering knowledge graph, analytics feature store, customer knowledge store) and a governed multi\-agent harness. The Principal AI Architect owns the technical shape of that platform.
What You'll Own\- You are the most senior individual contributor on the AI program. You make the load\-bearing technical decisions that everyone else builds against.
- Own the reference architecture for the AI plane end\-to\-end: data substrates, model layer, agent harness, orchestration, application surfaces, observability, and the ETL boundary to core platform and adjacent systems (payroll, benefits, claims, ADO).
- Drive build\-vs\-buy decisions on the substrate trio\- graph (Neo4j vs. TigerGraph vs. managed), vector (pgvector vs. Pinecone vs. Azure AI Search), feature store (Feast vs. Databricks Feature Store vs. Tecton), and warehouse direction (Azure Fabric vs. Snowflake)\- with written, defensible recommendations.
- Specify the multi\-agent harness: agent envelopes (planning, tools, reflection, memory), reward functions and hacking watchlists, HITL triggers, handoffs, capability gradients, and the deterministic Orchestrator that controls them.
- Define tenant isolation, identity resolution, and AuthZ for the Customer Knowledge Store across three access tiers (Client Admin, WSE, Internal CoAd)\- cross\-tenant leakage is a catastrophic\-tier risk and you own the controls that prevent it.
- Set the assurance program: input/output guardrails, red\-team plan, evals, circuit breakers, immutable lineage, and the production KG feedback loop.
- Partner with the Head of AI on the 18\-month roadmap and the platform consolidation thesis.
- Own the runtime execution architecture for the agentic platform, including orchestration topology, state management, workflow durability, retry semantics, and failure recovery strategies across distributed multi\-agent systems.
- Define platform\-wide evaluation and reliability standards, including groundedness metrics, hallucination detection, behavioral regression testing, drift monitoring, degraded operating modes, and production resiliency requirements.
- Establish governance standards for AI platform operations, including inference cost optimization, data contracts and lineage, tenant\-safe memory architecture, and security controls for regulated multi\-tenant environments.
- You will write specs, code, and prototypes. You will not be a deck\-only architect.
How We Work
- AI\-first coding. Claude Code, Copilot, and successor tools are the default development surface. Hand\-coding without AI assistance is the exception, not the norm.
- Build your own agentic workflows. You will compose and operate your own multi\-step agent pipelines\- for code generation, spec mining, design review, eval authoring, migration analysis. If a workflow doesn't yet exist for a job you do twice, you build it.
- Every workflow is testable. Every agent, every chain, every prompt that touches production has an eval harness, a regression suite, and a defined success criterion before it ships.
- Ambiguity is the job. You will get problems framed at the strategy level and return a sequenced, costed, testable plan with a working prototype.
- You estimate. Every recommendation comes with a timeline, a headcount ask, a confidence interval, and a list of the assumptions that could blow it up.
- You suggest the tools. Strong opinions, loosely held, written down.
First 90 Days
- Publish a v1\.0 reference architecture for the AI plane that the Head of AI signs and the CIO endorses.
- Deliver a build\-vs\-buy recommendation, with cost model and 18\-month TCO, for the substrate trio.
- Stand up a working prototype of one agent in the harness (Codi or Tespi from the v0\.4 spec) with its eval suite, reward function, and HITL path\- end\-to\-end on a real CoAd repo or ticket stream.
- Define the cross\-tenant leakage red\-team protocol for the Customer Knowledge Store and run the first round against the prototype.
- Co\-author the implementation timeline that converts the current planning blueprint into a sequenced, dependency\-ordered build plan.
Required Skills \& Experience
- 10\+ years building production software; 5\+ years architecting ML, LLM, or AI platforms at scale.
- Demonstrated ownership of a multi\-team AI platform\- including substrate decisions (vector, graph, feature store, warehouse), orchestration, and serving\- through one or more production releases.
- Working fluency with agentic coding tools (Claude Code, Cursor, Copilot, or equivalent) as a daily driver, with a body of work\- repos, specs, postmortems\- produced with them.
- Production experience with at least one multi\-agent system: orchestration patterns, tool calling, memory tiers, reward design, HITL, and the failure modes (collusion, reward hacking, loop runaway, capability creep).
- Hands\-on with vector search, knowledge graphs, and RAG at tenant\-scoped, regulated\-data scale.
- Strong opinions on eval design: regression suites, behavioral evals, red\-team protocols, training\-serving skew detection, drift monitors.
- Comfort writing executable timelines and headcount estimates and defending them in front of a CIO and a board.
- Cloud\-native infrastructure depth (Azure preferred given current CoAd footprint, AWS or GCP acceptable).
- Excellent technical writing\- specs, ADRs, ConOps, RFCs\- that non\-engineers can follow.
Preferred Experience
- Prior architect\-level work in PEO, HCM, payroll, benefits, insurance, or another regulated multi\-tenant SaaS.
- Experience operating under HIPAA, SOC 2, and state\-level payroll/tax regimes.
- Public work on agent safety, governance, or assurance (writing, OSS, research).
- Familiarity with Databricks, Azure AI Search, Snowflake, and modern observability for LLM systems (Langfuse, Phoenix, OpenTelemetry\-for\-LLMs).
- Experience leading a platform migration / consolidation under a hard deadline.
EEO
CoAdvantage is committed to providing equal employment opportunities to all employees and applicants without regard to race, color, religion, national origin, ancestry, citizenship status, age, sex (including pregnancy, childbirth, breast feeding and pregnancy\-related medical conditions), gender, gender identity or expression, sexual orientation, marital status, uniform service member and veteran status, disability, genetic information, or any other characteristic protected by applicable federal, state, or local laws and ordinances.
Benefits
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Health Insurance
Dental Insurance
Vision Insurance
401(k) Matching
Paid Time Off (PTO)
Paid Holidays
Remote Work
Bonus
Life Insurance
Salary Context
This $70K-$80K range is in the lower quartile for AI Architect roles in our dataset (median: $180K across 25 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 3,824 AI roles we're tracking, AI Architect positions make up 1% of the market. At CoAdvantage, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $220,000 based on 92 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($75K) sits 66% below the category median. Disclosed range: $70K to $80K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
CoAdvantage AI Hiring
CoAdvantage has 3 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Positions span US, Bradenton, FL, US. Compensation range: $80K - $90K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 median).
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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