Senior Backend Engineer, AI Platform

Accountabilities • AI Platform Development: Design, build, and scale the backend infrastructure required to deliver production-grade AI features across a platform serving millions of users.

• LLM Integration and Serving: Develop and maintain services that integrate multiple LLM providers, supporting model routing, failover, token streaming, and cost and usage tracking.

• Retrieval-Augmented Generation: Build and optimize retrieval systems, including vector search, embedding pipelines, context assembly, and citation-supported responses.

• Backend Architecture and APIs: Design robust APIs and backend services, taking end-to-end ownership of service architecture, schemas, implementation, deployment, and operational support.

• Reliability and Observability: Anticipate and address production risks such as hallucinations, retrieval failures, provider outages, latency issues, and unexpected costs through appropriate monitoring and instrumentation.

• AI Evaluation and Quality: Develop practical, repeatable evaluation methods to assess response relevance, safety, source quality, and overall AI performance.

• Production Operations: Monitor live services, troubleshoot incidents, improve system resilience, and support ongoing performance and reliability improvements.

• Cross-Functional Collaboration: Work closely with platform architects and fellow developers to shape technical decisions, establish engineering best practices, and integrate AI capabilities with existing application services.

Requirements

• Backend Engineering Experience: At least five years of backend engineering experience using a statically typed language such as Go, Java, Kotlin, or C#.

• Production LLM Expertise: Demonstrated experience shipping and maintaining LLM-powered features in production, including retrieval-augmented generation, streaming responses, and tool use.

• LLM Serving Infrastructure: Hands-on experience with model-serving systems, including multi-provider routing, failover strategies, token streaming, and cost and usage metering.

• Retrieval Systems: Experience developing vector search solutions, embedding pipelines, context assembly workflows, and systems that provide source-backed answers.

• Reliability and Observability: Strong understanding of AI failure modes and the ability to implement instrumentation, monitoring, and safeguards to detect and address production issues.

• AI Evaluation: Ability to assess AI output quality using straightforward, repeatable tests covering relevance, safety, and source quality rather than relying exclusively on academic benchmarks.

• API Design and Ownership: Strong API design skills and the ability to own backend services throughout their lifecycle, from data schema and implementation to deployment and on-call support.

• Location and Work Authorization: Must be based in the United States and authorized to work in the country.

• Preferred Programming Skills: Go experience is strongly preferred. Familiarity with Python is also beneficial.

• Preferred AI Infrastructure Experience: Experience with LLM gateways or serving frameworks such as LiteLLM, vLLM, TGI, or comparable technologies.

• Preferred Data and Model Experience: Familiarity with vector databases such as Qdrant or pgvector, embedding pipelines, and fine-tuning open-weight models using LoRA or full fine-tuning methods.

• Additional Advantages: Experience with content moderation or AI safety tooling, as well as familiarity with Ruby on Rails or React/TypeScript for application integration.

Benefits

• Remote Work: Full-time remote work for candidates based in the United States, subject to the role's location requirements.

• Technical Ownership: The opportunity to build greenfield AI infrastructure and influence architectural decisions, development practices, and platform capabilities.

• High-Scale Engineering: Work on AI-powered features designed for a platform serving millions of users.

• Modern Technology Stack: Exposure to Go, Ruby on Rails, Python, React/TypeScript, PostgreSQL, Redis, and RabbitMQ, with services deployed across multiple data centers.

• Professional Growth: Opportunity to deepen expertise in LLM infrastructure, retrieval systems, AI evaluation, distributed services, and production engineering.

• Collaborative Environment: Direct collaboration with platform architects and developers in a fast-paced engineering organization.

• Salary and Additional Benefits: Compensation, healthcare coverage, retirement benefits, paid time off, and other employee perks are not specified in the available job description and should be confirmed during the hiring process.

How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether?

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