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About This Role
Location
Our Applied AI Engineer will be an integral part of our global business systems team. This role is based remotely in the US East, the UK, Ireland, Estonia, the Netherlands, Sweden and Israel.
Who We Are
DoiT is a global technology company that works with cloud\-driven organizations to leverage the cloud to drive business growth and innovation. We combine data, technology, and human expertise to ensure our customers operate in a well\-architected and scalable state \- from planning to production.
Delivering DoiT Cloud Intelligence, the only solution that integrates advanced technology with human intelligence, we help our customers solve complex multicloud problems and drive efficiency.
With decades of multicloud experience, we have specializations in Kubernetes, GenAI, CloudOps, and more. An award\-winning strategic partner of AWS, Google Cloud, and Microsoft Azure, we work along
The Opportunity
We are bringing the "Full Stack Builder" model to DoiT's internal operations. As an Applied AI Engineer, you are a one\-person product team embedded directly into a core business function.
This is not an IT support role. You are not fielding tickets. You have a dedicated product surface \- your assigned department \- and a clear success metric: measurable process improvement, shipped fast. You will rely on your empathy and judgment to identify the friction, and you will use modern AI tooling to build, code, and deploy the automations end\-to\-end.
You live and breathe AI. You automate your own life for fun, you use AI agents as an extension of your own brain, and you get a genuine dopamine hit from taking a slow, manual process and turning it into a seamless, AI\-driven workflow.
What We're Looking For:
- The Solver Mindset: You can research a problem, design a solution, code it, and launch it. You use AI coding assistants heavily to multiply your own output.
- The AI\-Native Builder: You don't just know about AI. You build with it daily. You instinctively reach for tools like Claude Code, Gemini CLI and Codex to multiply your output.You are fluent in modern AI coding environments and autonomous agents like Claude Code.
- Strong Software Fundamentals: You produce production\-quality code, not just brittle scripts.
- AI Integration Experience: Hands\-on experience with LLM APIs, prompt engineering, RAG, MCP, and agent\-based architectures.
- Extreme Empathy: Translating messy, real\-world business workflows into clean technical solutions requires deep listening and communication skills. You can speak "Engineering" and business language equally well.
- Self\-Direction: There is no established playbook for this role. You will need to find the work, scope it, build it, and prove it mattered
Required Tech Stack \& Skills
AI \& LLM Stack
- AI Agents: you live and work inside AI agents like Claude Code, Gemini CLI, and Codex. This is not optional and not a nice\-to\-have. AI\-driven engineering is how we build at DoiT. You use AI agents as an extension of your brain to research, code, test, and deploy \- and you can critically evaluate the output. If you're not already shipping production code through AI agents daily, this role is not for you.
- LLM APIs: hands\-on experience with the Anthropic Claude API (primary) and/or OpenAI API, Google Gemini API
- Relentlessly Current: the AI stack moves weekly, not yearly. You follow model releases, new agent frameworks, protocol changes, and tooling updates as they happen. You don't wait for a blog post summary \- you read the changelog, try the beta, and know when to adopt and when to skip. Today it's MCP and Claude Code, six months from now it might be something else entirely. You'll be the first to know.
- MCP (Model Context Protocol): building or consuming MCP servers to connect AI agents to external tools and data sources
- Agentic Architectures: designing multi\-step agent workflows with tool use, decision\-making, human\-in\-the\-loop escalation, and guardrails
Core Development Stack
- TypeScript / JavaScript \- primary language for building internal tools and automations
- Next.js \- for building internal\-facing web applications and dashboards
- Python \- for data processing, scripting, and AI/ML workflows
- Firestore / Firebase \- primary database and backend services
- Google Cloud Platform \- Cloud Run, Pub/Sub, IAM
- Git / Github \- version control and collaborative development
Production \& Reliability
- API Design \- RESTful APIs, webhooks, secure authentication patterns
- Containerization \- Docker for packaging and deploying services
- CI/CD \- automated testing and deployment pipelines (GitHub Actions, Google Cloud Build)
- Observability \- logging, monitoring, and error tracking for AI\-powered systems
Required Qualifications
- 3–5 years of professional software engineering experience, including building and deploying production applications
- 1\+ year of hands\-on experience building multimodal LLM\-powered applications that run in production \- AI agents, agentic workflows, or LLM\-integrated tools (not just prompt experimentation)
- Early adopter of AI\-driven engineering \- Claude Code launched in May 2025\. We'd love to write "5\+ years of experience with AI terminal agents" here, but the tech is barely a year old. So instead: you were there from day one. You didn't wait for a tutorial \- you installed the CLI, broke things, and figured it out. If you remember when MCP had no docs, we are impressed.
- Demonstrated ability to take a project from idea to deployed product independently
Are you a Do'er?
Be your truest self. Work on your terms. Make a difference.
We are home to a global team of incredible talent who work remotely and have the flexibility to have a schedule that balances your work and home life. We embrace and support leveling up your skills professionally and personally.
What does being a Do'er mean? We're all about being entrepreneurial, pursuing knowledge, and having fun! Click here to learn more about our core values.
Sounds too good to be true? Check out our Glassdoor Page.
We thought so too, but we're here and happy we hit that 'apply' button.
Full\-time employee benefits include:
- Unlimited Vacation
- Flexible Working Options
- Health Insurance
- Parental Leave
- Employee Stock Option Plan
- Home Office Allowance
- Professional Development Stipend
- Peer Recognition Program
Many Do'ers, One Team
DoiT unites as *Many Do'ers, One Team*, where diversity is more than a goal—it's our strength. We actively cultivate an inclusive, equitable workplace, recognizing that each unique perspective enhances our innovation. By celebrating differences, we create an environment where every individual feels valued, contributing to our collective success.
\#LI\-Remote
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 DoiT, 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.
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.
DoiT AI Hiring
DoiT has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US.
Location Context
AI roles in Boston pay a median of $218,900 across 268 tracked positions. That's 19% above 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 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.
Frequently Asked Questions
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