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About This Role
OUR HIRING PROCESS:
- We will review your application against our job requirements. We do not employ machine learning technologies during this phase as we believe every human deserves attention from another human. We do not think machines can evaluate your application quite like our seasoned recruiting professionals—every person is unique. We promise to give your candidacy a fair and detailed assessment.
- We may then invite you to submit a video interview for the review of the hiring manager. This video interview is often followed by a test or short project that allows us to determine whether you will be a good fit for the team.
- At this point, we will invite you to interview with our hiring manager and/or the interview team. Please note: We do not conduct interviews via text message, Telegram, etc. and we never hire anyone into our organization without having met you face-to-face (or via Zoom). You will be invited to come to a live meeting or Zoom, where you will meet our INFUSE team.
- From there on, it's decision time! If you are still excited to join INFUSE and we like you as much, we will have a conversation about your offer. We do not make offers without giving you the opportunity to speak with us live.
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We are looking for an AI & Software Solutions Architect to guide architectural decisions across INFUSE's products and teams. You will review solutions built by internal teams and external vendors, establish shared engineering standards, guide legacy modernisation, and help ensure that production AI systems are reliable, secure and cost-effective.
As the Software Development department's sole Solutions Architect, you will report directly to the Director of Technology and work across a landscape of 500+ repositories, including backend services, LLM-based products and several legacy systems requiring ongoing modernisation. This is a senior individual contributor role with company-wide technical influence and no direct people management. Success in this role depends on strong technical judgement, clear communication and the ability to influence engineering decisions across teams. Your work will centre on architecture, reviews and cross-project improvements, alongside prototypes and selected hands-on development tasks. Engineering teams will deliver the broader implementation.
What you will do:
- Translate business needs into architectural decisions, explaining trade-offs, costs and risks and balancing technical quality with delivery priorities.
- Review code, architecture and vendor deliverables. Produce concise, actionable findings, prioritise improvements, and verify that critical issues have been resolved.
- Establish shared standards across projects, including security, documentation, service contracts and observability, and guide teams in adopting them.
- Define guardrails for production LLM and agentic systems, covering evaluation, prompt management, model routing, multi-provider access, security and cost control.
- Guide legacy modernisation initiatives, including application decomposition, authorisation modernisation, workflow migration and safe transition planning.
- Work with engineering and DevOps teams to advance Kubernetes adoption, improve deployment workflows and observability, and diagnose architectural bottlenecks.
- Build prototypes and reference implementations to validate decisions, and conduct technical interviews for engineering hires.
What you will bring:
- 5+ years in software engineering, including 3+ years owning architectural decisions for production systems as an architect, principal engineer or technical lead, with experience taking solutions from initial concept through implementation to production.
- Strong computer science and system design fundamentals for scalable, high-load distributed systems, including reliability, failure handling, observability and architectural trade-offs.
- Hands-on AWS experience designing and running production services, plus practical Kubernetes experience and the ability to work closely with DevOps on deployment and operational improvements.
- Experience delivering production LLM-based solutions, with an understanding of their lifecycle from business need and design through implementation and operation. Relevant experience includes RAG, tool calling, multi-provider integrations and cost optimisation.
- Experience evaluating and monitoring LLM systems across quality, latency and cost using observability and evaluation tooling such as Langfuse or similar platforms.
- Experience reviewing other teams' code and architecture and guiding legacy modernisation or migrations.
- A strong software engineering background, preferably with substantial Python and/or Node.js experience, and the flexibility to review and work across different stacks, including legacy PHP.
- Experience with PostgreSQL and NoSQL design and query optimisation, and asynchronous, event-driven systems using Kafka, RabbitMQ or similar brokers.
- Practical experience using agentic coding tools such as Claude Code, Codex or similar, with sound judgement about verifying generated code and assessing proposed changes.
- Clear technical writing and communication with engineers and non-technical stakeholders. You connect technical decisions to business goals, cost, risk and measurable outcomes, take responsibility for their consequences, and adjust your approach based on evidence.
- B2 or higher English for collaboration with distributed teams.
Nice to have:
- Experience with AIOps: applying AI to operational workflows, including anomaly detection, alert correlation, incident triage and observability.
- Understanding when classical ML is more appropriate than an LLM, and experience with MLOps.
- Experience with Keycloak or Azure AD SSO migrations.
- Experience with Backstage.
- Experience conducting vendor delivery audits.
- AWS Solutions Architect – Professional or Anthropic Claude certification.
We offer:
- We offer a competitive salary tailored to the candidate's experience and skills. Payments are processed monthly (USD)
- Full Remote. Work schedule: Monday-Friday, from 1:00 pm to 9:00 pm EEST (incl. 60-min breaks)
- Opportunity to work with cutting-edge AI and cloud solutions
- Access to the latest AI tools and premium subscriptions
- Long-term B2B collaboration
- Paid sick leave, vacation, and public holidays
- Reduced Fridays during the summer
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At INFUSE, 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 $212,000 based on 11,857 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 $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
INFUSE AI Hiring
INFUSE has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in ME, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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