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About This Role
Machine Learning Engineer
Location: US / Canada (Eastern Time) \- Home\-based
Job Type: Full\-time, Permanent
About AllCloud
AllCloud is a leader in amplifying organizations’ cloud potential through AI. With a track record of hundreds of successful migrations and implementations across AWS and Salesforce, AllCloud has developed strategies and solutions that enable businesses of all sizes to remain at the forefront of innovation.
AllCloud is a leader in AI\-led professional and managed services. As an AWS Premier and audited managed services Partner, and Salesforce Consulting partner, AllCloud provides comprehensive AI\-led cloud journey support, from initial migration to ongoing management through our Engage Managed Services. Our expertise ensures that clients remain aligned with ecosystem best practices while focusing on their core business growth.
AllCloud serves clients across the globe with offices in EMEA and North America. www.allcloud.io
Job Summary
We are looking for a savvy Machine Learning/Data Engineer to join our growing team of data experts. The hire will be primarily responsible for AI/ML projects on AWS, leveraging native services as well as custom\-built models to deliver predictive insights to our customers. In addition, this hire will also support migrating to the cloud, optimizing our customers’ databases and data flows, and enriching our operational and functional data flow with AI/ML algorithms.
The ideal candidate is confident in data in any form or scale and happy to learn and teach new data tools. The candidate enjoys optimizing data systems and building them from the ground up. The Machine Learning Engineer will support new system designs and migrate existing ones, working closely with solutions architects, project managers, and data scientists. They must be self\-directed and comfortable supporting the data needs of multiple teams, systems, and products. The right candidate will be excited by the prospect of optimizing or re\-designing our customers’ data architecture to support our next generation of products and data initiatives, and machine learning systems.
Responsibilities
- Keep our customers’ data separated and secure to meet compliance and regulations requirements.
- Design, Build and Operate the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and cloud (mainly AWS) migration and ‘big data’ technologies.
- Optimize various RDBMS engines in the cloud and solve customers' security, performance, and operation problems.
- Design, Build and Operate large, complex data lakes that meet functional / non\-functional business requirements.
- Optimize various data types ingestion, storage, processing, and retrieval from near real\-time events and IoT to unstructured data as images, audio, video and documents, and in between.
- Use Jupyter Notebooks to build and deploy ML models.
- Leverage AWS AI/ML pre built solutions to accelerate work for customers
- Work with customers and internal stakeholders, including the Executive, Product, Data, Software Development, and Design teams, to assist with data\-related technical issues and support their data infrastructure and business needs.
Requirements:
Summary of Key Requirements
- We seek a candidate with 3\+ years of experience in a Data Scientist/Machine Learning Engineer role who has attained a Bachelor's (Graduate preferred) degree in Computer Science, Mathematics, Informatics, Information Systems, or another quantitative field. They should also have experience using the following software/tools:
- Experience with big data tools: Spark, ElasticSearch, Hadoop, Kafka, Kinesis etc.
- Experience with relational SQL and NoSQL databases, such as MySQL or Postgres and DynamoDB or Cassandra.
- Experience with AWS cloud services: EC2, RDS, EMR, Redshift etc.
- Experience with functional and scripting languages: Python, Java, Scala, etc.
- Experience with various ML models for classification, scoring and more.
- Experience with Deep Learning Neural Networks (Convolution, NLP etc.)
- Experience with AWS AI/ML Services
- Experience with Python coding
- Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases.
- Experience building and optimizing ‘big data’ data pipelines, architectures and data sets.
- Strong analytic skills related to working with unstructured datasets.
- Build processes supporting data transformation, data structures, metadata, dependency and workload management.
- Working knowledge of message queuing, stream processing, and highly scalable ‘big data’ data stores.
- Experience supporting and working with external customers in a dynamic environment.
Certifications
- AWS Machine Learning Specialty (Strongly Preferred)
- AWS Solutions Architect \- Associate (Strongly Preferred)
Why work for us?
Our team inspires progress in each other and in our customers through our relentless pursuit of excellence; you will work with leaders who promote learning and personal development.
*AllCloud is an Equal Opportunity Employer and considers applicants for employment without regard to race, color, religion, sex, orientation, national origin, age, disability, genetics or any other basis forbidden under federal, provincial, or local law.*
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At AllCloud, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
AllCloud AI Hiring
AllCloud has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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