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About This Role
Ironistic is a creative web development agency dedicated to crafting exceptional digital experiences for clients across industries. We combine thoughtful strategy with cutting\-edge technology to deliver high\-impact web platforms and digital products. As we continue expanding our capabilities, we’re seeking a Senior Technical Lead with a passion for AI\-driven innovation to champion technical excellence and lead our production teams into the future.
Position Overview
Ironistic is searching for a visionary Senior Technical Lead – AI Innovation to lead and elevate our development team of 10–15 production developers, engineers, and technical contributors. This leadership role is ideal for a seasoned web technologist with deep, hands\-on experience in web development, AI integration, and scalable architecture, who thrives on guiding teams to build cutting\-edge solutions.
The Senior Technical Lead will be responsible for driving technical strategy, mentoring team members, and steering the adoption of AI\-augmented capabilities in client work and internal systems. As a key leadership contributor, you will work closely with Project Managers, UX/UI leaders, and the executive team to ensure technical quality, innovation, and timely delivery of high\-value products.
Apply online at https://www.ironistic.com/iron\-jobs/
Key Responsibilities
Leadership \& Team Management
- Lead, manage, and mentor a team of 10–15 developers, fostering growth, accountability, and high performance.
- Facilitate cross\-functional collaboration across project and creative teams.
- Set clear technical goals, performance expectations, and development pathways for team members.
- Drive recruitment and technical interviewing when scaling the team.
Technical Strategy \& Architecture
- Own high\-level architecture decisions for web and AI\-enabled systems.
- Establish best practices for coding standards, documentation, testing, DevOps workflows, and performance optimization.
- Evaluate emerging technologies and frameworks — especially in AI and machine learning — to recommend adoption that aligns with business goals.
AI Innovation \& Integration
- Lead innovation in AI\-assisted development tools, workflows, and client\-facing features.
- Build reusable AI frameworks, APIs, and components that can be integrated across client projects.
- Partner with product leadership to identify opportunities for AI\-driven improvements in client solutions.
- Promote best practices for the responsible and ethical use of AI technologies.
Delivery \& Quality Assurance
- Oversee technical planning, estimation, and delivery of complex features and projects.
- Conduct code reviews and enforce architectural consistency and quality.
- Support project teams in resolving technical challenges and mitigating risk.
- Champion scalable, secure, and resilient engineering practices.
Required Qualifications
- 8\+ years of web development experience, including hands\-on experience with modern web frameworks (e.g., React, Next.js, Vue, Node.js, etc.).
- 3\+ years in a leadership or technical lead role, with a proven record of managing and developing technical teams.
- Expertise in PHP is required.
- Deep understanding of software architecture, web performance, APIs, security, and modern DevOps practices.
- Demonstrated experience integrating AI/ML technologies into production systems — e.g., large language models (LLMs), recommendation engines, NLP, computer vision, or automation tools.
- A strong portfolio of shipped products and technical contributions.
- Excellent communication and interpersonal skills with the ability to influence both technical and non\-technical stakeholders.
Preferred Qualifications
- Experience with AI toolchains (OpenAI, LangChain, TensorFlow, PyTorch, Hugging Face, etc.).
- Familiarity with cloud platforms (AWS, GCP, Azure) and scalable architectures.
- Startup or agency experience where technical leadership influenced product direction and delivery.
- Experience implementing internal AI systems to accelerate development workflows.
Apply online at https://www.ironistic.com/iron\-jobs/
Job Type: Full\-time
Pay: $140,000\.00 \- $160,000\.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Health insurance
- Life insurance
- Paid time off
- Parental leave
- Professional development assistance
- Vision insurance
Experience:
- Web development: 8 years (Required)
- Leadership or Technical Lead Role: 3 years (Required)
- PHP: 2 years (Required)
Work Location: Remote
Salary Context
This $140K-$160K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 2064 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 3,963 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Ironistic, 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 $180,000 based on 12,398 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($150K) sits 17% below the category median. Disclosed range: $140K to $160K.
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 ($290,000) and AI Safety ($274,200). By seniority level: Entry: $97,760; Mid: $163,400; Senior: $227,400; Director: $244,800; VP: $250,000.
Ironistic AI Hiring
Ironistic has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $160K - $160K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,883 positions. About 15% of all AI roles offer remote work.
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 3,963 open positions tracked in our dataset. By seniority: 116 entry-level, 1,875 mid-level, 1,532 senior, and 440 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (593 positions). The remaining 3,349 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 ($290,000 median, 39 roles); AI Safety ($274,200 median, 52 roles); Research Engineer ($260,000 median, 421 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 3,963 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,783), Data Scientist (297), AI Software Engineer (279). 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 (116) are outnumbered by mid-level (1,875) and senior (1,532) 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 440 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (593 positions), with 3,349 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 $290,000 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 (2,043 postings), Aws (1,241 postings), Azure (934 postings), Rag (886 postings), Gcp (774 postings), Pytorch (614 postings), Prompt Engineering (614 postings), Claude (564 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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