Principal Architect - Agentic AI Platform

Role Profile

Hands-on builder-architect with 0->1 SaaS / AI product experience and startup exposure.

Role Overview:

CustomerInsights.AI is looking for a Principal Architect - Agentic AI Platform to help lead the next phase of architecture evolution for ciATHENA, our enterprise Agentic AI platform for Life Sciences Commercial. This is a hands-on architecture leadership role focused on evolving ciATHENA from a growing set of AI-enabled capabilities into a scalable, modular, secure and production-grade enterprise AI platform.

We are specifically looking for a 0->1 product builder who has helped design and build SaaS and/or AI products from an early stage through production. The ideal candidate will have worked in a startup, scale-up, product incubation or similarly entrepreneurial environment where architecture decisions had to be made with incomplete information, evolving requirements, limited resources and a strong bias toward execution.

The successful candidate is expected to move comfortably between whiteboarding, prototyping, code reviews, debugging and production architecture, and to work directly with engineering teams to implement key architectural changes.

Key Responsibilities:

• Own the current-state assessment and target-state architecture for ciATHENA.
• Define architectural principles and reference patterns across agent orchestration, multi-agent workflows, LLM/SLM integration, model abstraction and routing, RAG and enterprise knowledge architectures, memory/state, tool/function calling, human-in-the-loop workflows, AI evaluation, observability, security and governance.
• Drive architecture evolution of key platform capabilities including Agent Registry, Knowledge Spine, orchestration services and AI runtime components.
• Establish reusable platform patterns so new ciATHENA modules and client implementations do not require custom architecture each time.
• Define clear service boundaries, APIs, event patterns and integration standards across the platform.
• Design scalable and resilient patterns for multi-tenancy, session/state management, asynchronous processing, failure recovery, model fallbacks and distributed workloads.
• Work closely with existing Data Engineering and DevOps teams to ensure architecture is production-ready and deployable across CustomerInsights.AI-hosted and customer VPC environments.
• Define integration patterns with platforms such as Databricks, Snowflake, Veeva/Salesforce and enterprise data ecosystems.
• Partner with security and infrastructure teams on RBAC/RLS, SSO, auditability, tenant/data isolation, secrets management and enterprise security controls.
• Establish architecture standards around LLM independence and model portability, minimizing unnecessary dependency on any single model provider.
• Guide decisions around model selection, model routing, commercial LLMs, open-source models and smaller language models.
• Participate in architecture reviews and provide technical direction to AI, backend, platform and product engineering teams.
• Translate product and customer requirements into scalable platform capabilities rather than one-off implementations.
• Mentor senior engineers and help raise the architecture and engineering bar across the organization.

Must-Have Experience:

• Demonstrated 0->1 experience building SaaS, AI or data products rather than only maintaining or modernizing mature platforms.
• Experience working in a startup, scale-up, founding/early engineering team, internal incubation group or entrepreneurial product environment with significant ownership over architecture and engineering decisions.
• Experience taking products from concept / prototype -> MVP -> production -> scale.
• 8-10 years of software engineering / architecture experience, including ownership of complex cloud-native or distributed systems.
• Strong hands-on engineering background with evidence of personally building, prototyping and troubleshooting production systems.
• Ability to make pragmatic architecture decisions balancing speed to market, scalability, technical debt, security, reliability and cost.
• Comfort operating in environments where requirements are evolving and architectural boundaries are still being established.
• Experience creating reusable platform capabilities rather than solving each customer or use case independently.

Generative AI / Agentic AI Depth:

Hands-on experience designing and implementing production systems involving several of the following:

• LLM-based applications and Agentic AI architectures
• Multi-agent systems and workflow orchestration
• Tool/function calling, structured outputs and agent planning
• RAG, vector search and enterprise knowledge architectures
• Context engineering, agent memory and state management
• Model gateways, model routing and model fallback patterns
• AI guardrails, observability, tracing and evaluation frameworks
• Frameworks such as LangGraph, Semantic Kernel, AutoGen, LlamaIndex or similar

Engineering & Platform Depth:

• Strong understanding of microservices, APIs, event-driven architecture, distributed systems, state management, caching, resilience, scalability and multi-tenancy.
• Strong cloud architecture experience, preferably Microsoft Azure.
• Strong coding proficiency, preferably Python, with the ability to build and validate production-quality prototypes.
• Ability to conduct architecture, design and code reviews at a Staff / Principal engineering level.
• Comfort debugging across application, AI, data and cloud layers.
• Experience evaluating emerging AI technologies through hands-on experimentation rather than relying solely on vendor documentation.

Data & Enterprise Integration Experience:

• Working knowledge of Databricks, Snowflake, SQL/relational systems, vector databases, enterprise APIs, data warehouses/lakehouses, semantic layers and metadata/catalog architectures.
• Ability to design how enterprise data should be safely and efficiently exposed to AI agents without functioning as the primary Data Engineer.
• Experience integrating AI platforms with enterprise identity, data and application ecosystems.

AI Governance & Enterprise Readiness:

• Experience designing systems with authentication/authorization, RBAC/RLS, audit logging, tenant isolation, data privacy, AI guardrails, model governance, traceability, human oversight and monitoring/observability.
• Experience in regulated industries such as Life Sciences, Healthcare or Financial Services is a plus, but deep product and engineering capability is more important than domain pedigree.

Profile We Are Looking For:

We are looking for a builder-architect, not a traditional enterprise Solution Architect. The ideal candidate has likely spent part of their career in a startup, AI-native company, product organization, founding/early engineering team or internal incubation environment where they helped create something new from the ground up. They should combine the architectural depth of a Principal Engineer with the execution mindset of an early-stage product builder.

• AI-native startups and B2B SaaS startups / scale-ups
• Enterprise AI product companies and cloud/data platform organizations
• Founding or early engineering teams
• Internal incubation / innovation teams within larger technology companies
• Principal / Staff engineering roles with significant product ownership

Skills

APIs, Agentic AI, Azure, Databricks, Distributed Systems, Generative AI, LLM, LangGraph, Microservices, Multi-Tenancy, Python, RAG, SaaS, Snowflake, Vector Databases