Principal Applied AI Architect

Order.co•Boston, MA

13h•$225,000 - $275,000•Remote

About The Position

We are hiring a Principal Applied AI Architect to set how data and intelligence work at Order.co: the target-state architecture, the standards that keep it coherent, and the company-level bets that turn proprietary procurement data into durable advantage. This is a senior individual contributor role. You will identify company-level objectives, make the business case to fund them, and land production systems that move primary company KPIs. You will be the Head of Data's closest technical partner: the complementary technical leader to a strategy, product, and business lens. You keep those bets grounded in what we can actually do with the systems we have, and you are the person who knows what it would take to change that. You sit on the data team. Applied AI Scientists take the architecture into product initiatives and own the science through to the metric. Senior AI / Data Engineers take the same architecture into the platform and own the production path. You are the person who holds those two paths on one spine, and who can still get on the keyboard when the first systems need to exist. The work is architecture-first, with a real engineering bar. You will be fluent across stacks, treat security, reliability, and maintainability as delivery criteria rather than afterthoughts, and set a production quality standard that other teams can reuse. You will also stay a working applied scientist: statistical depth, model and agent judgment, and the ability to go from a strategic imperative to a shipped system. This is not an academic role. Understanding production systems and the business they serve is required, and you will get a change through operations and the other stakeholders who gate a real deploy.

Requirements

At least 14 years in applied data science, machine learning, data architecture, or production AI systems, with a track record of landing work that moved a company-level metric.
Experience as the technical counterpart to a product, strategy, or business leader, grounding the roadmap in system capabilities.
Deep working knowledge of production systems and the business they serve.
Ownership of target-state architecture across data and applied AI, including setting the spine, standards, and review process, and partnering with engineering to land it.
Repeated delivery of production systems personally designed, defended, and passed through operational and stakeholder gates, including serving, retrieval, evaluation, guardrails, and the operational loop.
Ability to size an initiative (direct and indirect impact, cost, risk, and return) and create a funded business case.
Real depth in current large language model and agent technology, including understanding where it works, how it fails, how to evaluate it, and when a simpler deterministic approach is better.
Strong quantitative foundation in experimentation, statistical reasoning, and causal thinking.
Fluency across stacks and a production engineering bar: infrastructure as code, CI/CD, observability, and security and reliability as delivery criteria.
Ability to work with warehouse, lake, and cloud data platforms as production software.
Heavy daily use of AI-native engineering workflows across design, coding, debugging, and review for at least the past 18 months, with judgment to know where to verify and what not to trust.
Working implementation proficiency across at least two cloud or technical ecosystems (e.g., AWS and GCP).
Evidence of mentoring senior individual contributors and setting standards reused by other teams.
Ability to align executives and technical leaders in ambiguous situations.

Nice To Haves

Experience with retrieval systems, vector search, ranking, recommendation, or production personalization.
Experience designing source-of-truth models, semantic layers, or master and reference data consumed by multiple product surfaces.
Experience setting model governance, monitoring, and responsible AI standards for an organization rather than a single initiative.
Experience in e-commerce, B2B procurement, vendor management, financial products, or heavy integration with external systems.
An advanced degree in a quantitative field.

Responsibilities

Define the target-state architecture for the data estate and for applied AI, aligned to business priorities rather than tooling fashion.
Establish source-of-truth models and a shared semantic picture of the business so fragmented sources converge into one reliable platform.
Set naming, modeling, lineage, and integration standards, and review new designs to ensure they align with the established spine.
Incorporate security, privacy, and compliance into the design, including classification, handling of sensitive data, access control, retention, and audit through every layer.
Defend cost, performance, and reliability as first-class constraints in the architecture.
Own where AI creates durable advantages for the data function, including build versus buy decisions, sequencing of initiatives, and what to deliberately skip.
Make AI demand a funded platform modernization story, including the data, contracts, features, and retrieval infrastructure required, sized in impact, cost, risk, and return.
Set institutional standards for evaluation, rollout, monitoring, model risk, and responsible AI.
Advise the Head of Data, and product and engineering leadership, on the capabilities, limitations, costs, and potential returns of the current stack before bets are made.
Set technical direction for the AI portfolio across initiatives.
Track the AI frontier by piloting what matters, killing what does not, and converting the rest into production at company scale.
Turn strategy into actionable projects and contribute at quarterly planning.
Keep stakeholders informed and remove blockers.
Direct a mix of human and agentic workstreams, accountable for the output of both.
Execute and implement, with roughly half the time dedicated to implementation.
Pass the same production gates as everyone else, understanding that operations, security, and stakeholders are integral to getting work into production.
Mentor Applied AI Scientists and other individual contributors.
Set the coherence standard (domain model, patterns, decision records) so work can be picked up and landed correctly by others.
Raise the production engineering bar in AI systems across teams, focusing on observability, resilience, and the operational loop for survivable launches.