Senior Backend Engineer, AI & Data Systems

Blueprint Technologies•Redmond, WA

7h•$135,000 - $145,000•Remote

About The Position

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.

Requirements

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.

Nice To Haves

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.

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.