Principal AI Systems Architect

Where this role sits

This is the most senior individual-contributor architecture role at Sapience AI. You own how the whole system fits together: how the KO graph, the COGENT architecture, the models, the serving layer, and the product surface combine into one coherent, scalable, trustworthy platform.

You work across every part of the stack, setting the architectural direction that lets specialized teams build fast without the system pulling apart at the seams.

You are a force multiplier: not the owner of one area, but the person who makes sure all the areas add up to a platform that can carry Sapience AI's ambitions for years.
Why this role exists

Collective intelligence spans hard subsystems: knowledge, reasoning, models, serving, and experience. Each can be strong on its own and still fail as a whole if the architecture does not hold together.

As the platform and the team grow, the cost of architectural drift compounds. Someone has to own the coherence of the system, the big technical bets, and the seams between teams.

The Principal AI Systems Architect owns that. You set cross-cutting direction, make the hard architectural calls, and keep MINERVA and COGENT coherent, scalable, and trustworthy as they grow.
What you will own (Areas of Responsibility)

You hold seven areas of responsibility across the platform architecture. Each one is yours to set direction on and be accountable for.
1. System architecture and coherence

• Own how the KO graph, COGENT, models, serving, and product surface fit into one coherent system.
• Set the cross-cutting architecture that lets teams build fast without drift.
• Own the seams between subsystems, where the hardest problems live.

2. Architectural strategy and big bets

• Make the architecture-level decisions that shape the platform for years.
• Weigh build, buy, and bet choices with a clear point of view.
• Keep the architecture aligned with where AI and the business are heading.

3. Scale, reliability, and trust by design

• Design for scale, reliability, and trust as properties of the architecture, not add-ons.
• Anticipate where the system will strain next and get ahead of it.
• Make security and governance structural.

4. Technical standards and patterns

• Set the standards and patterns that raise quality across the organization.
• Reduce accidental complexity and duplicated effort across teams.
• Make the right way the easy way.

5. Cross-team technical leadership

• Align specialized teams around shared architecture and interfaces.
• Resolve the hardest cross-team technical questions.
• Multiply the impact of every team through better structure.

6. Frontier evaluation and integration

• Evaluate frontier advances and decide how they fit the architecture.
• Separate durable direction from passing trends.
• Bring in what matters without destabilizing the system.

7. Mentorship and technical culture

• Raise the technical judgment of senior engineers across the organization.
• Model rigor, clarity, and honesty about trade-offs.
• Help the whole organization make better architectural decisions.

AI-augmented ways of working

You set the architecture for an AI platform and use AI to reason about it, to explore designs, stress-test trade-offs, and move faster, while holding the judgment that architecture at this level demands.

The standard is human in partnership: AI accelerates the work, you own the judgment, the interpretation, and the call. The people who create the most value here are not the ones producing the most output. They are the ones turning evidence into clear, durable decisions.
What this role is not

To keep the boundary clear:

• This is not a people-management role. You lead through architecture and influence, not through direct reports.
• This is not a single-subsystem role. You own coherence across the whole platform, not one area.
• This is not an ivory-tower role. You stay hands-on enough to be credible and to prove out the hard parts.
• This is not a research role. You are accountable for a production platform's architecture, not open-ended exploration.

What success looks like

We measure this role on outcomes the team can see:

• A coherent platform. The subsystems add up to one scalable, trustworthy system.
• Sound big bets. The architecture-level decisions age well.
• Trust by design. Scale, reliability, security, and governance are structural.
• Faster teams. Shared standards and patterns raise quality and speed across the organization.
• Durable direction. The architecture absorbs frontier advances without pulling apart.
• Stronger engineers. Senior engineers make better architectural calls because of you.

Who you are
Required qualifications

• Extensive experience as a senior or principal engineer or architect on complex production systems.
• Deep experience architecting AI or ML platforms end to end.
• A track record of architecture that scaled with a growing product and team.
• Strong systems, distributed systems, and data fundamentals.
• Sound judgment on security, reliability, and trust at the architecture level.
• The ability to lead through influence across many teams.
• Hands-on credibility that senior engineers respect.

Preferred qualifications

• Experience with knowledge graphs, neuro-symbolic systems, retrieval, or agentic AI.
• Experience owning inference and infrastructure at scale.
• Experience in trust-sensitive or regulated domains.
• A record of setting standards that lifted an organization.
• Experience integrating frontier advances into production architecture.

How you work

• You name the real architectural problem before reaching for a design.
• You hold a clear point of view and defend it with evidence.
• You make trade-offs explicit and own them.
• You treat trust, scale, and reliability as structural.
• You lead through influence and raise the people around you.
• You stay hands-on enough to be credible.

Skills & Competencies

• End-to-end AI platform architecture.
• Cross-cutting system design and interface definition.
• Scale, reliability, and trust by design.
• Technical standards and patterns.
• Frontier evaluation and integration.
• Technical leadership through influence.
• Mentorship of senior engineers.

Services & Tools Experience

• Distributed systems, data platforms, and ML infrastructure at an architectural level.
• LLM, retrieval, vector, and graph systems.
• Cloud platforms (AWS, GCP, or Azure), containers, and orchestration.
• Observability, security, and reliability tooling.
• Architecture and design tooling for complex systems.
• Enough hands-on coding to prototype and prove out hard parts.
• Deep architectural ownership of the MINERVA platform, KO graph, and COGENT architecture.

Prior Experience & Background

• Prior principal engineer or architect roles on large-scale AI or software systems.
• Experience carrying an architecture through significant growth.
• A track record of cross-team technical leadership.
• Experience in AI-native systems is strongly preferred.

Cross-functional partners

You work across the entire engineering organization, including Neuro-Symbolic AI, Applied AI, ML Infrastructure, Data and Knowledge, and Security, and you partner with the CTO on technical strategy. You own the coherence of the MINERVA platform and the COGENT architecture.
How we hire

We review every application, and we encourage you to apply even if you do not match every line above. Research shows that talented people, especially those from underrepresented communities, often hold back when they do not meet every qualification. If that is the only thing holding you back, apply anyway.

Compensation

Base Salary: $204,000 - $216,000 + early stage equity

Generous health and wellness benefits