Senior AI Engineer II – Global Commercial Services Technology

This is a fully remote position, open to applicants in North Carolina.

📋 Description

• Spearhead the technical design of new agent functionalities, transforming vague product intentions into deployed systems.

• Develop and manage services comprehensively, from event triggers through LLM reasoning to stored, visible outcomes.

• Enhance and define the shared agent framework, encompassing orchestration, tool utilization, structured generation, and observability.

• Design and optimize RAG and embedding pipelines utilizing vector search within the operational database.

• Create evaluations for new agent functionalities and regulate prompt modifications based on these evaluations.

• Ensure reliability, which includes failure classification, idempotency, DLQ management, and rollout safety for AI features in production.

• Establish standards for design and code reviews and mentor engineers who are integrating into the AI stack.

• Assess emerging models and techniques, incorporating effective ones into the platform.

• Work with technologies such as TypeScript, Go, Vercel AI SDK, Effect, gRPC, tRPC, Kafka, SQS, Lambda, EKS on AWS, Datadog, feature-flagged rollouts, and infrastructure as code.

⛳️ Requirements

• Over 6 years of experience building large-scale backend or distributed systems in a production environment.

• Successfully delivered LLM-powered features to actual users.

• Proficient in TypeScript or Go, with the ability to work comfortably across both languages.

• In-depth knowledge of distributed systems, including queues, event-driven design, failure modes, and idempotency.

• Ability to discern what decisions should be made by an LLM versus those made by code.

• Proven history of leading designs across a team.

• Excellent communication skills across engineering, product, and design teams.

• Contributions to open-source initiatives, particularly in AI, developer tooling, or infrastructure libraries (preferred).

• Experience in developing developer tooling, internal platforms, or frameworks for other engineers (preferred).

• Familiarity with designing LLM evaluations or managing LLM observability at scale (preferred).

• Experience with durable execution or workflow orchestration engines like Temporal (preferred).

• AI features implemented in financial services or other regulated sectors (preferred).

• Experience with vector search or embedding pipelines in production (preferred).

🏝️ Benefits

• Bonus

• Benefits