AI Platform Engineer

You own the operational foundations that make AI safe and maintainable-connectors into the Bronze layer, versioned interfaces, logging and auditability, evaluation, cost controls, and guardrails. This is an engineering role focused on reliability and lifecycle thinking, not a “light automation” position. You collaborate directly with internal stakeholders to translate needs into systems that hold up under real usage and evolve with the business. Essential Functions

• Design, build, and orchestrate multi-agent workflows (handoffs, coordination, retries/fallbacks, and failure handling) for business-critical use cases.

• Develop agents with role-appropriate personas, boundaries, and context so outputs are consistent, trustworthy, and aligned to business intent.

• Own Bronze-layer ingestion: build and maintain connectors/interfaces; manage schema drift, reliability, change handling, monitoring, and alerting.

• Treat data inputs/outputs as contracts-versioned, traceable, testable-and implement validation at data boundaries.

• Implement observability across the AI lifecycle (structured logs, traces, evaluation artifacts, and audit trails) so systems are debuggable and reviewable.

• Implement guardrails and controls: budgets, rate limits, model selection strategy, safe defaults, and kill-switches to prevent runaway behavior.

• Apply governance and access boundaries early (permissions, sensitive data handling, traceability, compliance posture) rather than bolting it on later.

• Produce durable documentation (architecture notes, runbooks, interface contracts) and enable others to operate and extend the platform.

• Provide evidence-based buy vs. build recommendations, and advocate for responsible sunsetting when systems reach end-of-life.

• Bachelor’s degree in computer science, data engineering, or a related field (or equivalent experience).

• 5+ years of software engineering and/or data engineering experience, including building and operating production services.

• Demonstrated experience deploying and supporting AI/LLM systems in production (monitoring, incidents, iteration, and measured improvement).

• Hands-on multi-agent orchestration experience (e.g., LangChain, AutoGen, CrewAI, or similar), including workflow design and failure handling.

• Experience owning connectors/ingestion pipelines (reliability patterns such as retries, idempotency, schema/version management, and alerting).

• Strong Python engineering skills; comfort working with APIs, data stores, and workflow/orchestration tooling.

• Operational discipline: logging, audit trails, debugging methodology, cost/token controls, and rollback mindset.

• Documentation-first habits (design notes, runbooks, interface contracts) and the ability to communicate tradeoffs to non-technical stakeholders.

• Preferred: Databricks/lakehouse + medallion familiarity; experience implementing governance/audit requirements; AEC or project-based domain exposure.

• Comfortable using AI-enabled productivity tools for meetings and knowledge capture (e.g., Fireflies AI Note Taker) while maintaining privacy and compliance boundaries. Physical Requirements

• Prolonged periods of sitting at a desk and working on a computer.

• Ability to communicate effectively in writing and verbally via phone, video conferencing, and in person.

• Visual acuity to perform responsibilities. Work Environment

We embrace a hybrid model that promotes both autonomy and collaboration, including the freedom to work from home, with regular in-office days to connect with teammates and build culture.