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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. The AI program runs three substrates (engineering knowledge graph, analytics feature store, customer knowledge store) and a multi\-agent harness. The Principal AI Architect designs the platform. The Staff MLOps Engineer makes it operationally real, repeatable, and safe to deploy.
What You'll Own\- You are the operational backbone of the AI platform
- Build and own the deployment pipelines for models, agents, prompts, evals, feature definitions, and KG/vector indices. Everything that touches production goes through a pipeline you wrote.
- Operate the feature store (offline \+ online), the knowledge graph infra (ADO\-KG and Customer Graph), and the vector indexing layer\- ingestion, materialization, freshness, drift, lineage.
- Stand up the eval harness as CI: every agent, prompt, and model change runs its eval suite on PR; a regression that breaks an eval blocks merge.
- Wire the observability plane: traces for every agent step, prompts and tool calls captured with PII redaction, cost and latency SLOs per surface, drift monitors, on\-call runbooks.
- Operate the HITL queue infrastructure\- routing, SLAs, audit, and the feedback loop back into evals and the KG.
- Own incident response for AI surfaces: cross\-tenant leakage, prompt injection, agent loop runaway, capability drift, KG poisoning. You write the runbooks and you carry the pager.
- Manage cost, capacity, and model routing across LM tiers (frontier vs. cheap\-and\-fast)\- agents should land on the right tier automatically, with budgets and circuit breakers.
- Own secrets, identity, and AuthZ enforcement at the infra layer\- tenant scoping must be enforced independently of the LLM, every time.
- You will write a lot of code. You will not be a "platform PM".
How We Work
- AI\-first coding. Claude Code, Copilot, and successor tools are the default development surface. We expect you to author pipelines, IaC, runbooks, eval harnesses, and operators with agentic coding tools in the loop.
- Build your own agentic workflows. Repetitive ops work\- incident triage, drift investigation, eval failure root\-cause, capacity forecasting\- gets automated as an agentic workflow you author and own.
- Every workflow is testable. Every pipeline, every agentic ops workflow, every runbook has tests: unit, integration, eval\-on\-PR, replay against a golden incident set.
- Ambiguity is the job. Specs from the Architect will be 80% complete on purpose. You fill the last 20% by shipping, instrumenting, and reporting back what the operational reality is.
- You estimate. Every workstream returns with a timeline, a confidence interval, an explicit list of dependencies, and the smallest version you could ship in a week.
- You suggest the tools. Specific opinions on orchestration (Dagster vs. Airflow vs. Prefect), serving, tracing, feature store, registry, and vector indexer\- and the willingness to defend them.
First 90 Days
- Ship the deployment pipeline for one agent end\-to\-end: code eval\-on\-PR staged rollout traced production drift monitor rollback. Used by the first production agent.
- Stand up eval\-as\-CI: PRs to any agent, prompt, or model run their suite automatically; failures block merge; results posted to the PR.
- Bring up an online feature store for the MLR Pricing / Premium Tiering model with a freshness watchdog and a fail\-closed posture on stale features.
- Define and implement the cross\-tenant leakage probe as a continuous CI check against the Customer Knowledge Store retrieval layer.
- Publish the incident runbook set for the four catastrophic\-tier risks (cross\-tenant leakage, prompt injection, KG poisoning, agent loop runaway) and rehearse one of them with the team.
Required Skills \& Experience
- 5\+ years of production software / platform engineering; at least 3 years operating ML or LLM systems at meaningful scale.
- Strong Python \+ at least one IaC stack (Terraform, Pulumi, Bicep). Comfortable in containers, Kubernetes, and a major cloud (Azure preferred).
- Working fluency with agentic coding tools (Claude Code, Cursor, Copilot, or equivalent) as a daily driver, and the code commits, IaC, or runbooks to show for it.
- Production experience operating at least one of: feature store, vector index, knowledge graph, LLM serving stack — with on\-call responsibility.
- Built and operated CI/CD for ML or LLM systems, including model/agent registries, eval gates, staged rollouts, and rollback.
- Hands\-on with observability for AI\- traces, prompt/output capture, PII redaction, cost telemetry, drift detection.
- Comfort designing and shipping testable agentic workflows\- composing tools, writing the eval, defining the success criterion, gating on it.
- Track record of carrying the pager and writing the postmortems.
Preferred Experience
- Experience under HIPAA, SOC 2, or state\-level payroll/tax compliance regimes.
- PEO / HCM / payroll / benefits / insurance domain familiarity.
- Production use of Databricks, Azure AI Search, Snowflake, Langfuse / Phoenix, Feast or Databricks Feature Store, Neo4j or TigerGraph.
- Experience automating ops work with agentic workflows you authored (not just consumed).
- OSS contributions to MLOps / LLMOps tooling.
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
============
Health Insurance
Dental Insurance
Vision Insurance
401(k) Matching
Paid Time Off (PTO)
Paid Holidays
Remote Work
Bonus
Life Insurance
Salary Context
This $80K-$90K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At CoAdvantage, 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($85K) sits 52% below the category median. Disclosed range: $80K to $90K.
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/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 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).
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 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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