Senior AI Engineer

Jeeves; Ottawa; Hybrid; CAD 120,000 - 180,000; Full time

Benefits offered by this job

Health & wellbeing

Learning & development

Flexible work

PTO & holidays

Job summary

Jeeves seeks a senior AI Engineer to own end-to-end AI workflows, designing agentic systems that combine extraction, reasoning and tool use. You will write design docs, choose models, and set success metrics for each workflow, building production-grade pipelines and clean backend integrations.

You will optimize costs and latency, implement observability, and ensure regulatory-grade audit trails while collaborating with Product and Backend teams.

Qualifications

• 7+ years of software engineering experience.
• Production experience with LLM/AI-powered systems.
• Strong Python and backend fundamentals (REST APIs, databases).
• Experience with observability (logging, tracing, dashboards).
• Excellent English communication.

Responsibilities

• Own end-to-end AI workflows from design to launch and iteration.
• Design agentic and multi-step AI systems with tool use and human review steps.
• Write design docs, model choices, and success metrics for each workflow.
• Build production-grade LLM pipelines with prompts, context, validation and fallbacks.
• Integrate AI services with Jeeves backend with robust contracts and data isolation.

Skills

LLM systems

Production AI

Python

REST APIs

PostgreSQL

Cloud (AWS/GCP/Azure)

Observability

Logging & tracing

English fluency

End-to-end ownership

Tools

OpenAI API

Anthropic API

LLM tooling

Job description

• As an AI Engineer at Jeeves, you will own whole workflows from end to end, not individual features. You’ll decide how to build it, ship it, measure it, and keep improving it. You’ll work closely with Product, Backend Engineering, and our customers, but you won’t wait for someone to hand you a spec
• This is a senior, hands-on role for people who are AI-native in how they work. You use coding agents and AI tools every day to move faster, and you know where they fall short. It isn’t a research role, and it isn’t a role for someone who wants a narrow, well-defined ticket queue
• Design agentic and LLM-based systems that combine extraction, retrieval, reasoning, and tool use to complete multi-step work, with clear human-in-the-loop points
• Write the design docs, make the build-vs-buy and model choices, and set the success metrics for each workflow you own
• Build production-grade LLM pipelines: prompt and context design, structured outputs, validation, fallbacks, and confidence scoring
• Integrate AI services cleanly with Jeeves’s backend: clear API contracts, retries, graceful degradation, and per-customer data isolation
• Manage cost and latency across models, choosing the right model for each step
• Build evaluation sets and automated evals for every workflow you own, and use them to catch regressions when prompts, models, or data change
• Instrument AI components with logging, tracing, dashboards, and alerts so you know when quality drops before a customer does
• Keep an audit trail of AI decisions that meets the standards of a regulated financial product
• Set patterns, shared tooling, and practices that other engineers can build on
• Show the wider team how to use coding agents and AI tools well in day-to-day engineering
• Review peers’ AI system designs and share what you learn openly

Benefits

• Health & Wellbeing: Comprehensive healthcare coverage available by region.
• Learning & Development: We value continuous growth through learning and offer our team various opportunities both internally and externally.
• Flexible Work: Work from anywhere in the world, remote and hybrid opportunities depending on location and department.
• PTO: Generous PTO, paid holidays
  and parental leave.

Hands-on experience shipping LLM-powered applications with APIs such as Anthropic, OpenAI, or similar, including structured outputs, error handling, and evaluationProfessional fluency in English, written and spokenExperience designing agentic or multi-step AI workflows (tool use, orchestration, human review steps)A track record of owning a large system or workflow end to end, from scoping and design through launch and iteration, with limited directionStrong 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 monitoring7+ years of professional software engineering experience, including at least 2 years building and operating LLM or AI-powered systems in production