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About This Role
We are seeking a hands-on AI Automation Specialist to join a fast-growing US-based healthcare purchasing organization. As the company's first growth hire, you will build its AI-driven sales engine from the ground up: prospecting, nurture, appointment setting and member engagement workflows powered by AI agents and tools such as Claude, Grok and HubSpot. You will work directly with the CEO, with a short path from idea to live workflow.
The ideal candidate has 5+ years of automation, RevOps or marketing ops experience, including real AI agents or LLM workflows they have built and put into production for sales or lead generation.
Responsibilities:
- Design and launch AI-driven outreach and nurture workflows that move cold B2B leads toward a qualified sales conversation.
- Build appointment-setting workflows that hand interested prospects to the sales manager.
- Build workflows that reach partner networks and generate interest at scale.
- Improve existing AI agents (churn and key account management), including the data they draw on, so their recommendations get sharper.
- Evaluate, prototype and deploy new AI agents for prospecting, follow-up, recall and member engagement, and retire what does not perform.
- Document each agent: what it does, its data sources, how to check its output, and who owns it.
- Connect AI tools to the sales team's email, calendar and CRM so routine prep, follow-up and logging happen faster.
- Train teammates on proven workflows and write short SOPs they can follow without help.
- Run focused 30-day pilots with clear goals, and track the funnel: contacts reached, responses, appointments booked and conversions.
- Check AI-generated work (data, calculations, claims) before it reaches a customer or partner.
- Keep CRM records and dashboards accurate, and deliver a concise weekly update to the CEO on progress, results and risks.
- Package proven workflows into a repeatable playbook that can be rolled out to new business units.
- Handle customer, pricing and partner information with strict discretion.
Qualifications:
- 5+ years of experience building automation, revenue operations, growth or marketing ops workflows.
- 1 to 2 years of hands-on work with LLM tools (Claude, ChatGPT, Grok or similar) in a business setting.
- Proven examples of AI workflows or agents you built and put into production for sales, lead generation or customer engagement. A portfolio or walkthrough is required.
- Working knowledge of automation platforms such as n8n, Make or Zapier, and connecting tools through APIs and webhooks.
- Hands-on CRM experience, ideally HubSpot: pipelines, sequences, properties and reporting.
- Comfortable with data: cleaning lead lists, building simple dashboards and measuring funnel conversion in Sheets/Excel or a BI tool.
- Excellent written and spoken English, confident working directly with a US-based CEO.
- Sound judgment about where AI output can be trusted and where it must be checked.
Strongly Preferred:
- Experience automating outbound or nurture campaigns for B2B audiences such as doctors or practice owners.
- Background in healthcare, optical, pharma, distribution or group purchasing.
- Light scripting in Python or JavaScript for custom integrations.
- Spanish proficiency.
Requirements:
- Updated Resume/CV and a portfolio or examples of AI workflows/agents you have built.
- Available to work US Eastern Time business hours (Monday to Friday, 9:00 AM - 5:00 PM ET).
- Ability to work in a fast-paced, dynamic environment.
- Reliable internet connection and personal computer.
- UPS/battery backup to ensure continuity during power interruptions.
Benefits:
- Competitive salary.
- WFH set-up.
- Career advancement opportunities.
- HMO.
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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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At CONCERTINA, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
CONCERTINA AI Hiring
CONCERTINA has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $208,000 across 317 tracked positions. That's 3% below 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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