Senior AI/ML Engineer

With over 30,000 customers, including a third of Fortune 500 companies, Tempo is trusted by organizations across the globe to make their workflows work better.

We create a suite of integrated solutions for time management, resource planning, budget management, roadmapping, program management, reporting and more. We create the tech that enables the modern team to deliver – for every step from first vision to value.

Since our beginning in 2007 as a project to make a time-tracking tool to help a client – Tempo has expanded to become the #1 time management add-on for Jira, and we have developed and acquired a multitude of tools to become one of the most trusted names in the Atlassian ecosystem.

We want everyone to work better – but we also want to be a tech company with a heart. Join us as we continuously innovate our award-winning products, create new solutions, and help the world work smarter, not harder.

About The Role We’re looking for a Senior AI/ML Engineer who will be working at the intersection of LLMs, real-time signal processing, and enterprise decision-making. This is not a research role or an isolated AI team position. You’ll sit alongside domain engineers as you ship production AI systems to enterprise customers.

This is not the typical AI/ML engineering role. We’re looking for someone who uses AI heavily in their own daily work, has built and shipped features powered by LLMs and autonomous agents, and has strong opinions on what good AI product engineering actually looks like. If your go-to move when facing a complex problem is to reach for an agent pipeline rather than a static script, you’re the kind of engineer we have in mind.

You’ll initially pair with our external AI partners to absorb their work and establish the patterns the broader engineering team will follow. Over time, you become the internal anchor for AI engineering — the person the rest turn to for prompt design, evaluation strategy, model selection, and agent architecture decisions.

What You’ll Do Signal detection and anomaly detection Statistical and ML-based detectors that identify meaningful patterns in portfolio signals — velocity changes, capacity saturation, dependency risks — from CDC event streams and external tool integrations. Not simple threshold alerts; intelligent pattern recognition that knows the difference between noise and a real problem.

Insight synthesis engine An LLM-powered correlation engine that takes raw signals and produces actionable insights with root causes, confidence scores, and evidence chains. Not just “something is wrong” — but “why it’s wrong, what it means, and what you should do about it.”

Planning Rules compiler A translation layer between natural language planning rules (written by portfolio managers) and the structured parameters that drive our Monte Carlo scheduling engine. The LLM interprets intent; the deterministic engine computes schedules. You’ll design how these two layers communicate reliably.

Evaluation and testing frameworks The pipelines that ensure AI outputs are reliable, consistent, and improving over time. Regression suites for prompt changes, A/B testing infrastructure for model updates, confidence calibration — because vibes-based testing doesn’t scale at enterprise scale.

MCP tool definitions LLM-ready tool specs for domain capabilities (Item Store queries, capacity lookups, scenario simulations) that Tempo AI can discover and invoke at runtime within a hub-and-spoke MCP architecture already in production.

Who You Are Must Have

• A track record of shipping LLM-powered features or products — prototypes don’t count; we want to see things that real users have relied on.
• Hands-on experience orchestrating agents — multi-step reasoning, tool use, autonomous action with guardrails. Frameworks like LangChain, LlamaIndex, CrewAI, AutoGen, or equivalent (including rolling your own).
• Deep LLM engineering fundamentals: prompt engineering, RAG architectures, function calling / tool use, context management, evaluation-driven development.
• Production-quality engineering practices — you write code with tests, participate in code review, care about CI/CD and observability. You build systems that run reliably in production, not notebook prototypes.
• Experience with event-driven or streaming data systems — CDC events, real-time pipelines, and the patterns that come with them.
• 5+ years i…