Interested in this AI/ML Engineer role at Blueprint Technologies?
Apply Now →Skills & Technologies
About This Role
About Blueprint
Blueprint is a technology solutions firm headquartered in Bellevue, Washington, with teams across the United States. We help organizations turn complex challenges into meaningful outcomes by connecting strategy and execution across AI, cloud, data, product development, and emerging technology.
Our culture is built by people who care deeply about doing exceptional work. We set high standards, take ownership, and continually challenge ourselves and one another to be better. We work hard, support each other, and take genuine pride in what we deliver for our clients, partners, and teams.
At Blueprint, you'll work alongside talented people with different experiences, expertise, and perspectives. You'll have opportunities to take on meaningful challenges, expand your skills, and see the impact of what you build.
Bring your perspective. Raise the standard. Build what matters.
In This Role
You will design, build, and maintain scalable backend systems and data infrastructure that support AI-powered product innovation. Working alongside engineering, research, design, and product partners, you will develop synthetic data-generation workflows, reliable data pipelines, and large-scale storage and retrieval solutions that enable rapid experimentation and the evaluation of intelligent systems. You will take ownership of technical work from design and implementation through testing, deployment, and ongoing support, applying strong software engineering practices, quantitative analysis, and cloud engineering principles to improve system quality, scalability, and reliability. This role offers the opportunity to solve complex technical challenges at the intersection of backend engineering, synthetic data, and AI.
Responsibilities
- Design, build, and enhance synthetic data-generation systems that transform structured inputs, persona information, and organizational context into coherent, traceable, and reproducible datasets.
- Develop, scale, and maintain reliable data pipelines, ETL workflows, and orchestration systems.
- Apply concurrency, asynchronous execution, and resilient failure-handling techniques to improve pipeline performance and reliability.
- Design and optimize data models, storage architectures, partitioning, indexing, and retrieval patterns to support large data volumes and efficient access.
- Build data validation frameworks and automated quality checks to measure data accuracy, consistency, completeness, and fitness for use.
- Apply quantitative analysis, data profiling, and performance measurements to investigate system behavior and improve quality, scalability, and reliability.
- Integrate and support AI and large language model (LLM) workflows, considering model capabilities, limitations, output quality, and evaluation requirements.
- Apply cloud engineering practices to deploy, monitor, operate, and support dependable backend systems while balancing scalability, cost, and operational simplicity.
- Assess existing codebases, evaluate technical trade-offs, and translate evolving requirements into maintainable, modular, and well-tested solutions.
- Collaborate with engineering, research, design, and product partners to develop practical solutions and communicate progress, technical decisions, and risks.
- Maintain clear technical documentation, automated tests, deployment guidance, and operational practices that support long-term system ownership.
Required Qualifications
- 5+ years of software engineering, backend engineering, or data engineering experience, or equivalent significant hands-on experience building and supporting production systems.
- Strong backend programming and systems engineering skills, including designing maintainable services, modular components, APIs, and automated tests.
- Experience with programming languages and frameworks such as Python, Java with Spring Boot, or C#/.NET.
- Demonstrated experience designing, building, scaling, and maintaining data-generation systems, ETL workflows, distributed data-processing solutions, and cloud-based data pipelines.
- Working knowledge of large language models (LLMs), including their capabilities, limitations, common failure modes, and the impact of data quality, prompting, and evaluation on AI-powered systems.
- Understanding of workflow orchestration, concurrency, asynchronous execution, streaming or event-driven architectures, and failure recovery.
- Experience with large-scale data storage, data modeling, and retrieval, including partitioning, indexing, access patterns, and storage architecture trade-offs.
- Strong analytical and troubleshooting skills, including the ability to validate data and system behavior, interpret performance measurements, and make evidence-based engineering decisions.
- Practical cloud engineering experience and the ability to work independently in an existing codebase.
- Ability to take ownership of technical solutions from design through implementation, testing, deployment, and ongoing support.
- Strong technical communication, collaboration, documentation, and problem-solving skills.
- A disciplined approach to software quality, maintainability, and operational reliability.
Preferred Qualifications
- Hands-on experience with Python for backend development, data processing, automation, or AI-related workflows.
- Experience integrating LLMs through AI platforms, frameworks, and APIs.
- Experience developing and evaluating production-oriented AI applications and LLM-based workflows.
- Experience designing LLM evaluation frameworks, defining quality criteria, and implementing automated checks to assess generated outputs.
- Familiarity with distributed processing, streaming, event-driven systems, and workflow orchestration technologies such as Apache Spark, Kafka, or equivalent platforms.
- Experience with relational and NoSQL databases, cloud object storage, and data lake architectures.
- Knowledge of observability, performance optimization, deployment automation, and production system monitoring.
- Experience building automated tests, data-quality validation frameworks, and reproducible data workflows.
- Ability to navigate ambiguous requirements, explore technical alternatives, and adapt to changing priorities.
- Experience collaborating across engineering, research, design, and product disciplines in an iterative, experimentation-driven environment.
- Prior experience with Microsoft technologies or products is a plus but is not required.
- A portfolio or technical work samples demonstrating relevant engineering experience is a plus but is not required.
Salary Range
At Blueprint, we strive to offer competitive pay that reflects the value of our team members. Compensation for this role is influenced by a variety of factors, including skills, education, responsibilities, experience, and geographic market. For candidates based in Washington State, the anticipated salary range is $135,000– $145,000 USD/Annually. Please note that we typically do not hire new employees at the top of the posted range. Actual starting pay will be determined based on experience, skills, and internal equity. The final salary and job title may vary depending on the selected candidate's qualifications and could fall outside the stated range.
Equal Opportunity Employer
Blueprint Technologies, LLC is an equal employment opportunity employer. Qualified applicants are considered without regard to race, color, age, disability, sex, gender identity or expression, orientation, veteran/military status, religion, national origin, ancestry, marital, or familial status, genetic information, citizenship, or any other status protected by law.
If you need assistance or a reasonable accommodation to complete the application process, please reach out to: [email protected]
Blueprint believe in the importance of a healthy and happy team, which is why our comprehensive benefits package includes:
- Medical, dental, and vision coverage
- Flexible Spending Account
- 401k program
- Competitive PTO offerings
- Parental Leave
- Opportunities for professional growth and development
Location: Remote
Benefits
Blueprint believes that healthy, supported employees do their best work. Eligible employees have access to a comprehensive benefits package that may include:
- Medical, dental, and vision coverage
- Flexible Spending Account (FSA)
- 401(k) retirement plan
- Competitive paid time off
- Parental leave
- Professional growth and development opportunities
Benefits and eligibility may vary based on role, employment status, and location.
Equal Employment Opportunity
Blueprint Technologies, LLC is an equal opportunity employer. We consider qualified applicants without regard to race, color, religion, sex, pregnancy, childbirth or related medical conditions, sexual orientation, gender identity or expression, national origin, ancestry, age, disability, genetic information, marital or familial status, military or veteran status, citizenship status, or any other characteristic protected by applicable law.
Applicant Accommodations
If you need a reasonable accommodation to participate in any part of the application or interview process, please contact [email protected].
Salary Context
This $135K-$145K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Blueprint Technologies, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($140K) sits 34% below the category median. Disclosed range: $135K to $145K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Blueprint Technologies AI Hiring
Blueprint Technologies has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Redmond, WA, US, Charlotte, NC, US. Compensation range: $115K - $145K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.