Senior AI Engineer, AI Foundry - MAIA Platform

About the role

MAIA is Prophet's next leap in AI - an always-available assistant that amplifies human insight. By integrating directly into our workflows, it turns AI into true Augmented Intelligence, extending our creativity, judgment, and expertise. Built on a multi-agent framework, MAIA connects specialized tools, data sources, and reasoning systems through one interface, so our people can do more, think deeper, and deliver sharper outcomes for clients. As a Senior AI Engineer on the AI Foundry team, you'll help define and drive the direction of MAIA, Prophet's multi-agent AI platform. This is a senior individual-contributor role: you lead through technical depth and influence rather than direct reports. You'll take ambiguous business and client needs and decide what's worth building, own capability areas end to end, make the architectural calls the team builds against, and carry work all the way through to reliable, production-grade delivery. Like every Foundry role, this one sits at the intersection of technical and non-technical worlds - but at a higher altitude. You'll be the most technical person in most rooms, advise Prophet and client leadership directly, serve as a primary interface to our external engineering partners, and raise the technical bar for everyone around you.

Key facts

What you'll do

Take fuzzy business and client needs and define what's worth building and why, then design and run the experiments to prove it out, owning go/no-go calls and setting the direction others execute against.

Own the architecture of agents and their data flows and prompt systems, setting the patterns, standards, and reusable building blocks the rest of the team builds on.

Make and own the architectural calls for platform capabilities including knowledge graph design, API integration patterns, and RAG strategies, and define how the team evaluates tradeoffs across cost, speed, accuracy, and maintainability.

Take capabilities from prototype to production, owning deployment, reliability, monitoring, cost optimization, test coverage, and latency/error-rate outcomes across client-facing applications.

Advise Prophet and client leadership on what's feasible and what's worth doing, shaping the platform roadmap and owning the working relationship with external engineering partners.

Raise the whole team's bar by setting standards, authoring playbooks and internal guidance, reviewing others' work, and mentoring engineers (without formal management responsibility).

Translate developments in LLMs, agent frameworks, and AI tooling into a clear point of view on what MAIA should adopt and when, using deep technical grounding to judge maturity and risk.

Own end-to-end delivery of specific MAIA capabilities, coordinating with product, data, and research partners to turn concepts into reliable, measurable outcomes.

Establish evaluation frameworks and benchmarks for agent behaviors and system performance, ensuring we can measure and compare alternatives objectively.

Champion security, privacy, and compliance considerations in system design, ensuring implementations meet our standards and client requirements.

Drive the selection and integration of tooling for observability, eval, and testing, ensuring the platform provides clear insight into agent behavior and system health.

Evangelize best practices across the Foundry, running sessions and reviews that elevate how Prophet and our partners design and ship AI-powered experiences.

Requirements

Bachelor's or advanced degree in Computer Science, Machine Learning, Data Science, Engineering, or an equivalent technical background.

~3-5 years building and shipping production AI/ML or software systems.

Deep Python plus strong SQL across systems like PostgreSQL, Snowflake, or BigQuery, and fluency with Pandas/NumPy.

Demonstrated ownership of production LLM and agent systems - multi-agent orchestration (LangChain/LangGraph/LlamaIndex), advanced RAG, prompt systems, and observability/eval tooling such as LangSmith.

Strong MLOps and cloud practice: Docker, CI/CD, infrastructure-as-code (e.g., Terraform), and AWS/GCP/Azure, including serverless (e.g., Lambda) and async architectures.

A solid ML/DL foundation (PyTorch incl. Lightning, TensorFlow/Keras, scikit-learn) and the judgment to know when classical ML beats an LLM.

API and integration architecture - REST design, OAuth 2.0, and connecting multiple enterprise systems and data sources.

Cost and latency optimization as a discipline, with a track record of measurable production impact.

A track record of turning ambiguous, poorly defined problems into shipped, reliable solutions.

Excellent communication and stakeholder-management skills, including advising leadership and coordinating with external technical teams.