Description

Senior Backend & Data Engineer (Contract)

Please note that this is a contract role providing services to Microsoft through external staffing partners of Allegis Global Solutions. If you are selected for this role, you will be employed by AGS and will not be an employee of Microsoft.

Summary

We are seeking an experienced Senior Backend & Data Engineer to design, build, and evolve scalable backend systems and data infrastructure supporting next-generation AI-powered experiences. This is a hands-on engineering role focused on synthetic data generation, cloud-based data pipelines, distributed processing, large-scale storage, data quality, and LLM-enabled workflows. The successful candidate will take ownership of technical initiatives from assessment and architecture through implementation, testing, deployment, and ongoing support. This role requires strong software engineering fundamentals, deep experience building production data systems, and a practical understanding of Large Language Models (LLMs) and AI workflows. The ideal candidate is comfortable working within existing codebases, navigating ambiguity, evaluating technical trade-offs, and collaborating closely with engineering, research, design, and product partners. This is a fully remote position operating Monday through Friday, 40 hours per week, with availability required during a portion of Pacific Time business hours.

Job Responsibilities

- Design, build, and evolve synthetic data-generation systems that transform structured inputs and contextual information into coherent, traceable, and reproducible datasets.
- Develop and maintain scalable backend services, data pipelines, ETL workflows, and distributed data-processing systems.




- Build and operate orchestration frameworks utilizing concurrency, asynchronous execution, resilient failure handling, and recovery mechanisms.
- Design and optimize data models, storage architectures, retrieval strategies, partitioning approaches, indexing, and access patterns for large-scale datasets.
- Integrate Large Language Models into backend and data workflows using AI platforms, frameworks, APIs, and related technologies.
- Develop evaluation and validation frameworks that define quality criteria, automate data checks, and measure the quality and consistency of system outputs.
- Apply quantitative analysis and data profiling techniques to evaluate system performance, investigate issues, and improve reliability.
- Build, deploy, monitor, troubleshoot, and maintain cloud-based backend and data systems with an emphasis on scalability, operational reliability, and cost efficiency.
- Work within existing codebases to identify technical gaps, improve architecture, resolve defects, and implement maintainable solutions.
- Evaluate engineering trade-offs and translate evolving or ambiguous requirements into practical technical solutions.
- Create and maintain automated tests, technical documentation, operational guidance, and engineering standards.
- Collaborate with engineers, researchers, designers, strategists, and product partners to prototype, evaluate,



and deliver innovative AI-powered solutions.
- Communicate technical progress, risks, dependencies, design decisions, and recommendations to engineering and cross-functional stakeholders.

Requirements

- 5+ years of professional software engineering, backend engineering, data engineering, or equivalent experience building and supporting production systems.
- Strong backend programming experience using languages and frameworks such as Python, .NET, Spring Boot, or comparable backend technologies.
- Demonstrated experience designing maintainable backend services, modular components, APIs, and automated tests.
- Deep hands-on experience designing, building, scaling, and operating data generation, ETL, and distributed data-processing workflows.
- Experience building and maintaining cloud-based data pipelines and large-scale data systems.
- Strong working understanding of Large Language Models (LLMs), including model capabilities, limitations, common failure modes, prompting, data quality, evaluation, and model behavior.
- Experience integrating or evaluating LLM-based workflows, AI platforms, frameworks, or APIs.
- Working knowledge of orchestration, concurrency, asynchronous execution, distributed processing, streaming or event-driven architectures, and resilient failure recovery.
- Strong knowledge of large-scale data storage, modeling, and retrieval, including partitioning, indexing, access patterns, and storage architecture trade-offs.
- Robust quantitative analysis, debugging, and troubleshooting skills with the ability to validate data and system behavior and make evidence-based engineering decisions.
- Practical cloud engineering experience, including buil

📌 Data Engineer (Redmond)
🏢 Microsoft
📍 Redmond