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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Backend Engineer - AI & Data Systems - **Company:** TEAM Inc. - **Location:** Redmond, WA, United States (Remote available) - **Experience:** Expert - **Salary:** $168,480.0 - $176,800.0 - **Contract:** Temporary to permanent - **Skills:** Java (Programming Language), .NET Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Data Analysis, Automation of Tests, Big Data, Cloud Computing, Cloud Database, Cloud Engineering, Profiling, Computer Programming, Databases, Data Validation, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Systems, Relational Databases, Distributed Computing Environment, Python (Programming Language), NoSQL, Performance Tuning, Software Engineering, Data Streaming, Systems Integration, Workflow Management Systems, Data Processing, Office365, Large Language Models, Concurrency, Database Optimization, Apache Spark, Spring-boot, Model Validation, Reliability of Systems, Indexer, Backend, Event Driven Architecture, Build Management, Data Lakes, AI Platforms, Storage Technologies, Deployment Automation, Apache Kafka, Data Pipelines, Automation Anywhere, Data Generation - **Published:** October 18, 2026 - **Apply:** https://www.dice.com/job-detail/b38c3857-8831-4b15-97cb-d84d21ea6f0a ## About the Role 1. Backend Software Engineering (5+ years): Strong hands-on experience developing production backend systems using Python, Java/Spring Boot, .NET, or similar technologies. Must demonstrate ownership of scalable services, APIs, modular architectures, automated testing, and ongoing system support. 2. Data Engineering, ETL & Cloud Pipelines: Substantial experience designing, building, and maintaining data-generation systems, ETL workflows, and distributed cloud-based data pipelines. Must understand orchestration, concurrency, asynchronous processing, and resilient failure handling. 3. LLM & AI Systems: Strong working knowledge of large language models, including capabilities, limitations, prompting, model behavior, and common failure modes. Experience integrating or evaluating LLM-based workflows and implementing automated quality checks. 4. Large-Scale Data Architecture & Validation: Experience with data modeling, storage, retrieval, and performance optimization for large datasets. Ability to develop validation frameworks, analyze data quality, troubleshoot system behavior, and make evidence-based engineering decisions., * 5+ years of professional software engineering, backend development, or data engineering experience building and supporting production systems. * Strong programming skills in Python, Java/Spring Boot, .NET, or comparable backend development languages and frameworks. * Demonstrated experience designing maintainable backend services, APIs, modular software components, and automated testing frameworks. * Extensive hands-on experience with data generation, ETL processes, distributed data processing, and scalable cloud-based pipelines. * Strong understanding of LLMs, including model capabilities, limitations, prompting, evaluation methodologies, and AI workflow integration. * Experience with workflow orchestration, asynchronous processing, concurrency, streaming or event-driven architectures, and failure recovery. * Knowledge of large-scale data modeling, storage, partitioning, indexing, retrieval optimization, and database architecture. * Strong analytical and troubleshooting skills, including quantitative performance measurement, data validation, and root-cause analysis. * Practical cloud engineering experience, including deployment, monitoring, and operational support of production systems. * Ability to work independently within established codebases, assess existing implementations, and deliver maintainable improvements. * Strong communication, documentation, collaboration, and problem-solving skills, particularly in cross-functional engineering environments. * Experience with Apache Spark, Kafka, or equivalent distributed processing and event-driven technologies is preferred. * Familiarity with relational databases, NoSQL systems, cloud object storage, and data lake architectures is preferred. * Experience integrating LLMs through AI platforms, frameworks, or APIs and evaluating production-oriented AI workflows is preferred. * Experience with observability, performance tuning, and deployment automation is a plus. * Previous Microsoft experience is a plus but is not required. What makes a candidate highly successful in this role: A highly successful candidate combines strong backend software engineering fundamentals with hands-on experience building sophisticated data pipelines and a practical understanding of modern AI systems. You'll bring the technical depth to independently assess existing architectures, identify opportunities for improvement, and deliver scalable solutions that remain reliable as requirements evolve. Success requires more than implementing individual features. You'll be comfortable owning complex engineering challenges from initial assessment and design through development, testing, deployment, and ongoing support. Experience building synthetic data-generation systems, integrating LLM-based workflows, and developing automated evaluation frameworks will be particularly valuable. The strongest candidates are naturally curious, adaptable, and comfortable operating in environments where experimentation and rapid iteration are essential. You'll communicate technical trade-offs clearly, collaborate effectively across engineering and product disciplines, and apply sound judgment to balance innovation with operational reliability. ## Description Team Red Dog is hiring a Senior Backend Engineer - AI & Data Systems for our client, a Fortune 50 technology leader and AI-driven technology organization. In this role, you'll design and build scalable backend systems, synthetic data-generation workflows, and cloud-based data pipelines that support the development of next-generation AI-powered enterprise experiences. Working at the intersection of software engineering, large language models (LLMs), and data infrastructure, you'll develop reliable systems for data processing, orchestration, storage, and automated quality validation. Your work will directly support innovation in Microsoft 365 Copilot, helping engineering and product teams prototype, evaluate, and deliver intelligent experiences at enterprise scale., This is an opportunity to contribute to the future of AI-powered productivity experiences by building the backend infrastructure that supports Microsoft 365 Copilot innovation. You'll join a collaborative, multidisciplinary team of engineers, designers, researchers, strategists, and product partners exploring how intelligent systems can become more useful, trustworthy, and intuitive. Rather than focusing solely on traditional backend maintenance, you'll help shape the systems that enable rapid AI prototyping, synthetic data generation, model evaluation, and product experimentation. This role offers significant technical ownership, opportunities to solve complex engineering challenges, and the ability to influence how emerging AI technologies are evaluated and incorporated into enterprise products. How you will make an impact: * Design, develop, and enhance scalable synthetic data-generation systems that transform structured inputs, persona information, and organizational context into coherent, traceable, and reproducible datasets. * Build and maintain cloud-based data pipelines, ETL workflows, and distributed processing systems that support large-scale data generation and analysis. * Develop orchestration workflows incorporating concurrency, asynchronous execution, and robust error handling to improve pipeline reliability and scalability. * Design and optimize data models, storage architectures, indexing strategies, and retrieval patterns to efficiently manage large datasets. * Build automated validation frameworks and data-quality checks to measure accuracy, consistency, reproducibility, and overall system performance. * Apply quantitative analysis, profiling, and troubleshooting techniques to identify bottlenecks, investigate system behavior, and improve operational efficiency. * Develop, deploy, monitor, and support reliable backend services using sound cloud engineering and software development practices. * Evaluate architectural approaches and technical trade-offs to balance performance, scalability, maintainability, and cost. * Collaborate with engineers, researchers, designers, and product stakeholders to translate evolving requirements into practical technical solutions. * Maintain automated tests, technical documentation, and operational guidance to support long-term system reliability and maintainability.