Senior AI Engineer

Jeeves

. Own AI workflows end to end, including deciding how to build, ship, measure, and improve them .

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

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building and operating LLM-powered systems, with a focus on end-to-end workflow ownership, API integration, and observability. Proficient in Python and backend development, with a strong emphasis on designing effective AI workflows and maintaining compliance in regulated environments.

Highest-signal resume keywords

LLM Pipeline DevelopmentAI Workflow DesignPython ProgrammingAPI IntegrationObservability for AI Systems

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

LLM DevelopmentAI System DesignPythonREST APIsPostgreSQLCloud ComputingError HandlingAutomated EvaluationLoggingTracing

Soft Skills

Effective CommunicationTeam CollaborationMentoring

Tools & Technologies

AWSGCPAzureAnthropic APIOpenAI API

Industry Keywords

AI WorkflowsHuman-in-the-LoopRegulated Financial ProductCost ManagementLatency Management

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformPostgresPython

About the role

Key responsibilities & impact

Own AI workflows end to end, including deciding how to build, ship, measure, and improve them
Design agentic and LLM-based systems combining extraction, retrieval, reasoning, and tool use with clear 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 and use them to catch regressions
Instrument AI components with logging, tracing, dashboards, and alerts
Maintain an audit trail of AI decisions for a regulated financial product
Set shared AI engineering patterns, tooling, and practices
Teach the wider team effective use of coding agents and AI tools
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