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About This Role
About Future Tech
Future Tech Enterprise, Inc. delivers enterprise technology solutions and services to commercial, federal, and global clients. We work with leading technology partners to help organizations modernize operations and apply emerging technology to real business problems.
Position Overview
Most companies are still talking about how AI may change the way people work. Future Tech is looking for an engineer who wants to build that future now.
We are building a small, startup\-style AI engineering team with a simple mandate: find valuable internal problems, build AI solutions quickly, put them in the hands of real users, and learn from what happens. As an Applied AI Engineer, you will work directly with the Chief Technology Innovation Officer, executives, and employees to turn rough ideas into working applications. You will own the full path from discovery and prototype through deployment, feedback, and iteration.
This role is designed for a true builder \- someone who experiments constantly, learns new tools quickly, and would rather demonstrate working software than spend months discussing it. Future Tech will provide access to the latest models, AI coding tools, frameworks, infrastructure, and partner expertise needed to move fast. Examples may include Claude Code, Hugging Face, Thinking Machines Inkling, frontier level open and closed models, GCP, and emerging agent frameworks, but the stack will evolve with the technology and the problem.
What You Will Build* AI applications and agents that help employees complete real work, make decisions, find information, and automate repetitive processes.
- Fast prototypes that test promising ideas with users before the company makes a larger investment.
- Production\-ready internal tools that connect AI with business data, systems, workflows, and user interfaces.
- Reusable components and patterns that make each new AI solution faster and easier to deliver.
What You Will Do* Work directly with users to understand problems, identify high\-value opportunities, and define what should be built.
- Design and build full\-stack AI solutions across the user experience, application logic, models, data, APIs, integrations, and deployment.
- Select the right technical approach for each problem rather than forcing every use case into the same framework or platform.
- Ship early versions quickly, gather feedback, measure whether they work, and improve or abandon them based on what you learn.
- Move successful prototypes into reliable production solutions with appropriate security, permissions, testing, monitoring, and human oversight.
- Stay current on a rapidly changing AI ecosystem and bring useful new capabilities into the team when they create practical value.
Qualifications \& Requirements* Demonstrated ability to build working software through professional experience, internships, open\-source work, or a strong portfolio of personal projects.
- Proficiency in at least one modern programming language and the ability to work across APIs, databases, back\-end services, and front\-end interfaces.
- Hands\-on experience building with large language models or AI APIs, not only using consumer AI applications.
- Strong curiosity, bias toward action, and comfort learning unfamiliar technologies while solving ambiguous problems.
- Ability to communicate with non\-technical users, translate their needs into a solution, and respond constructively to feedback.
- Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent practical experience. Unconventional paths are welcome.
- This position works with US Government Contractors, therefore U.S. Citizenship is required.
Preferred Skills* Experience building an end\-to\-end AI application that includes a user interface, application services, model integration, data, and deployment.
- Experience with AI coding tools such as Claude Code, Cursor, or Codex.
- Familiarity with full\-stack AI patterns such as tool calling, retrieval\-augmented generation, agent orchestration, reasoning loops, feedback loops, evaluations, observability, and guardrails.
- Experience integrating applications with enterprise APIs, identity and access controls, databases, cloud services, or workflow platforms.
- A portfolio, GitHub profile, open\-source contribution, hackathon project, or other work that shows what you like to build.
How This Team Works* Thoughtful speed over unnecessary process.
- Working software and real user feedback over lengthy requirement documents.
- Small experiments, rapid learning, and a fail\-fast/fail\-forward mindset.
- Freedom to use the best available technology for the problem.
- High ownership: the person who builds the solution stays close to the users and the outcome.
Why Join Future Tech* Build AI solutions that will be used by executives and employees, not prototypes that disappear after a presentation.
- Work directly with the Chief Technology Innovation Officer and have meaningful ownership from day one.
- Experiment with cutting\-edge technology without being constrained by a fixed corporate stack or lack of access to tools.
- Develop rapidly by shipping real products, seeing how users respond, and learning what works in an enterprise environment.
- Help define how Future Tech builds and scales practical AI solutions across the business.
- Competitive benefits, including medical, dental, vision, 401(k) with company match, and paid time off.
Employment decisions at Future Tech Enterprise, Inc. will be based on merit, qualifications, and abilities. Future Tech Enterprise, Inc. does not discriminate in employment opportunities or practices on the basis of race, color, religion, sex, national origin, age, disability, or any other characteristic protected by law.
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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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Future Tech Enterprise Inc, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Future Tech Enterprise Inc AI Hiring
Future Tech Enterprise Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Fort Lauderdale, FL, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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