Senior AI Engineer

Jeeves

. Own AI workflows end to end, including design, shipping, measurement, and continuous improvement .

Posted 10/12/2026full-timeRemote • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and operating LLM-powered systems, with a focus on end-to-end workflow ownership, design, and continuous improvement. Proficient in integrating AI services, managing performance metrics, and ensuring compliance in regulated environments.

Highest-signal resume keywords

LLM Pipeline DevelopmentPython ProgrammingAI Workflow DesignAPI IntegrationObservability for AI Systems

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

LLM DevelopmentPythonREST APIsPostgreSQLCloud Computing (AWS, GCP, Azure)LoggingTracingDashboardsQuality MonitoringError Handling

Soft Skills

CommunicationCollaborationTeaching

Tools & Technologies

Anthropic APIOpenAI API

Industry Keywords

AI SystemsAgentic SystemsHuman-in-the-LoopRegulated Financial Product

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformPostgresPython

About the role

Key responsibilities & impact

Own AI workflows end to end, including design, shipping, measurement, and continuous improvement
Design agentic and LLM-based systems combining extraction, retrieval, reasoning, and tool use, with human-in-the-loop points
Write design documents, make build-vs-buy and model choices, and set workflow success metrics
Build production-grade LLM pipelines with prompt and context design, structured outputs, validation, fallbacks, and confidence scoring
Integrate AI services with Jeeves's backend using clear API contracts, retries, graceful degradation, and per-customer data isolation
Manage cost and latency across models
Build evaluation sets and automated evaluations to catch regressions
Instrument AI components with logging, tracing, dashboards, and alerts
Maintain an audit trail of AI decisions for a regulated financial product
Establish shared AI engineering patterns, tooling, and practices
Teach the wider team to use coding agents and AI tools effectively
Review peers' AI system designs and share learnings

Requirements

What you’ll need

7+ years of professional software engineering experience, including at least 2 years building and operating LLM or AI-powered systems in production
A track record of owning a large system or workflow end to end, from scoping and design through launch and iteration, with limited direction
Hands-on experience shipping LLM-powered applications with APIs such as Anthropic, OpenAI, or similar, including structured outputs, error handling, and evaluation
Experience designing agentic or multi-step AI workflows (tool use, orchestration, human review steps)
Strong proficiency in Python, plus solid backend fundamentals: REST APIs, PostgreSQL or similar relational databases, async patterns, and a major cloud provider (AWS, GCP, or Azure)
Experience with observability for AI systems: logging, tracing, dashboards, and quality monitoring
Professional fluency in English, written and spoken