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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Backend Engineer, AI & Data Systems - **Company:** Blueprint Technologies - **Location:** Redmond, United States (Remote available) - **Experience:** Expert - **Salary:** $135,000.0 - $145,000.0 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), .NET Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Systems Engineering, Automation of Tests, Big Data, C Sharp (Programming Language), Cloud Database, Cloud Engineering, Software Quality, Data Validation, Information Engineering, Extract Transform Load (ETL), Data Profiling, Distributed Computing Environment, Distributed Data Store, Python (Programming Language), Microsoft Software, NoSQL, Performance Tuning, Software Engineering, Data Streaming, Systems Integration, Workflow Management Systems, Data Processing, Large Language Models, Apache Spark, Spring-boot, Indexer, Backend, Event Driven Architecture, Data Lakes, AI Platforms, Storage Technologies, Deployment Automation, Apache Kafka, Evaluation of Large Language Models, Data Pipelines, Programming Languages - **Published:** October 12, 2026 - **Apply:** https://startup.jobs/senior-backend-engineer-ai-data-systems-blueprint-technologies-10443137 ## About the Role * 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. ## Description * 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.