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About This Role
Job Overview
We are seeking a high\-output AI Builder to join our "AI Factory" and revolutionize how we deliver consulting services. This is not a research role or a strategy position; it is a hands\-on execution role. We need a developer\-operator who can dismantle complex consulting workflows and rebuild them into high\-velocity, AI\-augmented systems. If you thrive on "shipping over slides" and building autonomous agents that solve real\-world problems, we want you.
2\. Engagement
- Status: Direct W2 Contractor (No C2C)
- Timeline: Immediate start \| 6–12 month initial engagement (high potential for extension).
- Location: Remote\-friendly, with a strong preference for candidates in the Texas Triangle (Dallas or Austin) to align with core team.
3\. Core Mission
As an Claude AI Factory Builder, your success isn't measured by lines of code, but by the adoption and impact of the artifacts you ship. You will identify high\-leverage automation points within professional services and deploy Claude\-powered agents and GenAI workflows that move the needle for our clients and internal teams.
4\. What You Bring \- The Prolific Builder
- Proven Portfolio: You have a GitHub or portfolio full of deployed AI tools and functional agents. You don't just talk about LLMs; you build with them.
- End\-to\-End Ownership: You take a vague problem statement, architect the solution, build the agent, and iterate based on user feedback.
Applied GenAI Technical Stack
- Claude Expert: Deep, hands\-on experience with the Anthropic ecosystem (Claude Code, Markdown\-based development, and Prompt Engineering).
- Agentic Orchestration: Experience building multi\-agent workflows, autonomous tools, and integration layers that connect AI to real business data.
- Tool Agnostic: While we love Claude, you are an experimenter who stays on the pulse of emerging GenAI frameworks.
The "Execution\-Hybrid" Mindset
- Ambiguity is Your Playground: You don't need a 50\-page requirements document. You can translate "this process is slow" into a concrete automated workflow.
- Bias for Action: You choose experimentation and functional prototypes over heavy documentation and theory.
5\. The Ideal Candidate
- Shipped over Theoretical: You can point to at least three AI\-enabled artifacts currently in use.
- Agentic Specialist: You understand the difference between a chatbot and an autonomous agent.
- Systems Thinker: You see the "Consulting Workflow" as a series of data points and leverage triggers.
- Lone Wolf \& Collaborator: You can work independently to solve a bug but communicate your logic with absolute clarity to stakeholders.
*We are an equal opportunity employer and value diversity. All employment is decided on the basis of qualifications, merit, and business need*.
Pay: $85\.00 \- $95\.00 per hour
Application Question(s):
- Are you currently authorized to work in the United States on a W2 basis without the need for visa sponsorship now or in the future?
- Are you currently based in Texas (specifically the Dallas or Austin areas) ?
- Please describe a specific AI\-enabled tool or autonomous agent you have personally built and deployed. What was the specific problem and the outcome?
- On a scale of 1–10, how would you rate your hands\-on experience with Claude\-based development (Anthropic API, Claude Code, and Markdown\-based prompting)?
- We prioritize rapid prototyping and shipping over heavy documentation. Are you comfortable operating in a high\-velocity environment where requirements are fluid?
Work Location: Remote
Salary Context
This $176K-$197K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At VAMS Technologies, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($187K) sits 12% above the category median. Disclosed range: $176K to $197K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
VAMS Technologies AI Hiring
VAMS Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in TX, US. Compensation range: $197K - $197K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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.
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