Senior AI Engineer

Jeeves

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

Posted 10/15/2026full-timeRemote • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and operating LLM-powered systems, with a strong focus on AI workflow management, integration, and observability. Proficient in Python and backend technologies, ensuring high-quality, production-grade AI solutions.

Highest-signal resume keywords

LLM Pipeline DevelopmentAI Workflow ManagementPython ProgrammingREST API DesignObservability for AI Systems

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonLLM DevelopmentAI Systems DesignREST APIsPostgreSQLCloud ComputingError HandlingAutomated EvaluationLoggingTracing

Soft Skills

CollaborationCommunicationProblem-Solving

Tools & Technologies

AWSGCPAzureAnthropicOpenAI

Industry Keywords

AI WorkflowsHuman-in-the-LoopProduction-Grade SystemsCost ManagementRegulated Financial Products

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 continuously improve them
Work closely with Product, Backend Engineering, and customers
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 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 catch regressions when prompts, models, or data change
Instrument AI components with logging, tracing, dashboards, and alerts
Maintain an audit trail of AI decisions meeting regulated financial product standards
Set patterns, shared tooling, and practices for other engineers
Demonstrate 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
At least 2 years building and operating LLM or AI-powered systems in production
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, including tool use, orchestration, and human review steps
Strong proficiency in Python
Solid backend fundamentals: REST APIs, PostgreSQL or similar relational databases, async patterns, and a major cloud provider such as AWS, GCP, or Azure
Experience with observability for AI systems, including logging, tracing, dashboards, and quality monitoring
Professional fluency in English, written and spoken

Benefits

Comp & perks

AI-assisted tools support application review, interview scheduling, and notes
Accommodation or alternative to AI-assisted steps available upon request