Senior AI/ML Engineer

Role overview Build and ship production AI capabilities that help enterprise teams detect delivery risks, understand their causes, and make better portfolio plans. Working alongside product engineers, you’ll shape how language models, agents, statistical methods, and deterministic planning systems work together. You’ll also establish practical patterns for AI engineering across the wider team.

Responsibilities - Develop statistical and machine learning detectors that distinguish meaningful changes in delivery signals from routine noise. - Build language model workflows that connect signals, explain likely causes, and present evidence, confidence, and useful next steps. - Design a reliable translation path from natural language planning rules to structured inputs for a scheduling engine. - Create evaluation and testing pipelines, including prompt regression checks, model comparisons, and confidence calibration. - Define tools that let AI systems query domain data and run capacity or planning scenarios. - Pair with external AI specialists initially, then guide the team on prompts, model choices, evaluations, and agent design.

Requirements - At least five years in software engineering, including three or more years building AI or machine learning systems in production. - A record of delivering LLM-powered features that real users depend on. - Practical experience building agents with multi-step reasoning, tool use, and safeguards. - Strong grounding in prompt design, retrieval-augmented generation, function calling, context management, and evaluation. - Production engineering habits, including tests, code review, CI/CD, and observability. - Experience with event-driven or streaming data, and the ability to work day to day with product, design, and domain engineering colleagues. - Proficiency in Python and either Kotlin or TypeScript.

Nice to have - Experience with a managed LLM platform such as AWS Bedrock, Azure OpenAI, or Google Vertex AI; Model Context Protocol or similar tool-use patterns. - Background in anomaly detection, Monte Carlo simulation, optimization, or scheduling. - Familiarity with portfolio, project, or resource planning; enterprise SaaS concerns such as tenancy, compliance, and audit trails. - Contributions to open-source AI or machine learning projects.

Benefits and work setup Remote-first work, health, dental, vision, and savings benefits, vacation flexibility in most locations, and support for training and home-office costs are listed. The role involves close collaboration with product engineers and an initial handover from external AI partners.