Senior AI Engineer II – Global Commercial Services Technology

American Express

. Lead the technical design of new agent capabilities, from ambiguous product intent to shipped system .

Posted 9/22/2026full-timeRemote • North Carolina • United StatesSenior💰 $123,000 - $215,250 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing large-scale backend systems, with a focus on LLM-powered features and distributed systems. Proficient in TypeScript and Go, with a strong emphasis on reliability, observability, and mentoring within AI frameworks.

Highest-signal resume keywords

Large-Scale Backend SystemsLLM-Powered FeaturesTypeScriptGoDistributed Systems Knowledge

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Event-Driven DesignFailure ModesIdempotencyRAG PipelinesEmbedding PipelinesDesign EvaluationsAI Feature ReliabilityDurable ExecutionWorkflow Orchestration

Soft Skills

Clear CommunicationJudgment in Decision MakingMentoring

Tools & Technologies

Vercel AI SDKGRPCTRPCKafkaSQSLambdaEKS on AWSDatadogFeature-Flagged RolloutsInfrastructure as Code

Industry Keywords

AI Features in Financial ServicesRegulated IndustryOpen-Source ContributionsDeveloper ToolingInternal Platforms

Tech Stack

Tools & technologies

AWSDistributed SystemsGRPCKafkaTypeScriptGo

About the role

Key responsibilities & impact

Lead the technical design of new agent capabilities, from ambiguous product intent to shipped system
Build and operate services end to end, from event trigger through LLM reasoning to persisted, surfaced results
Extend and shape the shared agent framework, including orchestration, tool use, structured generation, and observability
Design and tune RAG and embedding pipelines on vector search in the operational database
Design evaluations for new agent behaviors and gate prompt changes on them
Own reliability, including failure classification, idempotency, DLQ handling, and rollout safety for AI features in production
Set standards in design and code review and mentor engineers ramping onto the AI stack
Evaluate emerging models and techniques and integrate effective ones into the platform
Work with TypeScript, Go, Vercel AI SDK, Effect, gRPC, tRPC, Kafka, SQS, Lambda, EKS on AWS, Datadog, feature-flagged rollouts, and infrastructure as code

Requirements

What you’ll need

6+ years building large-scale backend or distributed systems in production
Shipped LLM-powered features to real users
Strong TypeScript or Go, with comfort working across both
Strong distributed-systems knowledge: queues, event-driven design, failure modes, and idempotency
Judgment about what an LLM should decide versus what code should decide
Track record of driving designs across a team
Clear communication across engineering, product, and design
Contributions to open-source projects, especially AI, developer-tooling, or infrastructure libraries (nice to have)
Experience building developer tooling, internal platforms, or frameworks other engineers build on (nice to have)
Experience designing LLM evaluations or operating LLM observability at scale (nice to have)
Experience with durable execution or workflow orchestration engines such as Temporal (nice to have)
AI features shipped in financial services or another regulated industry (nice to have)
Vector search or embedding pipelines in production (nice to have)

Benefits

Comp & perks

Bonus
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