Backend AI Data Engineer

Team are seeking a hands-on senior backend engineer with deep expertise in data generation and processing, scalable pipelines, large-scale storage, and quantitative analysis. The ideal candidate combines strong software engineering fundamentals with practical experience building and maintaining reliable, cloud-based backend systems.

The candidate will take ownership of technical work from assessment and design through implementation and ongoing support. This role requires sound engineering judgment, comfort working in existing codebases, and the ability to translate evolving requirements into maintainable solutions while collaborating closely with engineering and cross-functional partners., Design, build, and evolve synthetic data-generation systems, assessing existing approaches and technical trade-offs while developing scalable, maintainable workflows that translate structured inputs, persona and organizational context into coherent, traceable, and reproducible datasets.

Build and operate scalable pipelines and orchestration, applying concurrency, asynchronous execution, and resilient failure handling where appropriate.

Design and evolve data models, storage approaches, and retrieval patterns to support large data volumes and efficient access.

Develop and evolve validation frameworks and systems that define quality criteria, automate checks, and use quantitative analysis and profiling to assess data quality, investigate system behavior, and drive improvements in performance and reliability.

Apply cloud engineering practices to build, deploy, monitor, and support dependable systems, balancing scalability, operational simplicity, and cost.

Evaluate technical trade-offs, communicate progress and risks, and collaborate on practical solutions. Maintain clear documentation, automated tests, and guidance that support ongoing ownership.

You’re a self-driven backend engineer who cares about building reliable, scalable foundations for thoughtful product experiences. You can turn evolving ideas into maintainable services and data workflows, learn new tools quickly, and are comfortable exploring the unknown. You value strong engineering practices and know how to collaborate across engineering, design, and product.

You contribute openly, prototype or experiment to learn, share feedback generously, and help raise the quality of the team’s work. You are curious, adaptable, and motivated to build systems that make a real impact., Experience with Python

Experience integrating LLMs through AI platforms, frameworks, and APIs, including building or evaluating production-oriented AI workflows.

Experience designing and running evaluations of LLM output, such as defining quality criteria, building automated checks.

Experience with distributed processing, streaming or event-driven systems, and workflow orchestration tools-such as Apache Spark, Kafka, or equivalent technologies-and familiarity with relational or NoSQL databases, cloud object storage, or data lake architectures

Experience with observability, performance tuning, and deployment automation is also valuable; no specific cloud provider or storage product is required

Experience with Apache Spark, Kafka, or equivalent distributed-processing and event-driven technologies.

Familiarity with relational or NoSQL databases, cloud object storage, and data lake architectures.

Ability to work independently within an existing codebase and translate evolving requirements into maintainable solutions.

Strong technical communication, documentation, collaboration, adaptability, and problem-solving skills.

Experience creating automated tests and data-quality validation frameworks

Portfolio/work samples are optional, 5+ years of software or data engineering experience, or equivalent significant experience building systems with coding in languages & frameworks including, but not limited to, Python, Spring Boot, .NET, with demonstrated ownership through implementation and support.

• Strong backend programming and systems engineering skills, including designing maintainable services, modular components, APIs, and automated tests.

• Deep experience designing, building, and operating data generation, ETL, and distributed data-processing workflows, including creating, scaling, and maintaining cloud-based pipelines.

• Strong working understanding of LLMs, including their capabilities, limitations, common failure modes, and the role of data quality, prompting, evaluation, and model behavior in AI-powered systems.

• Working knowledge of orchestration, concurrency, asynchronous execution, streaming or event-driven architectures, and recovery from failures.

• Strong knowledge of large-scale data storage, modeling, and retrieval, including partitioning, indexing, access patterns, and trade-offs among storage approaches.

• Strong quantitative analytical and troubleshooting skills, with 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. Clear technical communication, effective collaboration, and a disciplined approach to documentation and operational reliability.

Everforth Apex is a world-class IT services company that serves thousands of clients across the globe. When you join Everforth Apex, you become part of a team that values innovation, collaboration, and continuous learning. We offer quality career resources, training, certifications, development opportunities, and a comprehensive benefits package. Our commitment to excellence is reflected in many awards, including ClearlyRateds Best of Staffing in Talent Satisfaction in the United States and Great Place to Work in the United Kingdom and Mexico.

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