AI Engineer; AI Enablement Platform

About the Team

We are building a shared AI Enablement Platform that allows product teams to ship AI‑powered features quickly, safely, and  platform provides reusable APIs for chat, summarization, RAG, classification, and AI workflow execution; agent orchestration patterns; evaluation and validation systems; observability, policy enforcement, cost controls; developer experience and documentation.

As a Staff AI Engineer – AI Enablement Platform, you will be a core technical builder of the platform, writing production code, designing reusable services, reviewing designs, debugging issues, and partnering with product teams. The role is hands‑on and focuses on building real systems rather than research or training models from scratch.

• Build the AI platform foundation – write and ship production code for core services, APIs, orchestration components, evaluation systems, SDKs, and internal developer tools.
• Design, build, and operate reusable capabilities such as chat APIs, RAG services, summarization, classification, semantic search, prompt/workflow execution, and agent orchestration.
• Create high‑quality APIs, SDKs, templates, and reference implementations that product teams can adopt with minimal friction.
• Develop production‑ready orchestration patterns for retrieval, tool use, validation, fallback handling, memory/state management, and human‑in‑the‑loop workflows.
• Review PRs, debug production issues, improve reliability, and make pragmatic technical trade‑offs.
• Partner with SRE, Security, Platform, and Product Engineering teams to ensure reliability, scalability, security, observability, and cost‑awareness.
• Own evaluation, reliability, and production readiness – build harnesses for LLM‑powered systems, implement AI observability practices, design safe defaults, and ensure strong production fundamentals.
• Help teams move from prototypes to production by identifying gaps in reliability, observability, security, evaluation, and operational readiness.
• Drive AI adoption across ABC – establish patterns, standards, documentation, code examples, architecture reviews, and technical enablement.
• Lead technical design reviews and architecture forums for AI systems, ensuring sound decisions around quality, safety, reliability, and maintainability.
• Identify and convert repeated friction into reusable platform capabilities and raise AI engineering capability through internal demos, enablement sessions, and technical write‑ups.
• Act as a hands‑on technical leader – set technical direction, align stakeholders, mentor engineers, and ensure responsible use of AI‑assisted engineering tools.

Not a data‑science role focused on analysis or dashboards; not a research role focused on training foundation models; not a prompt‑only role; not one‑off AI features for a single product line; not an architecture‑only role; not a people‑management role.

• 10+ years of hands‑on backend/platform engineering experience.
• Proven experience building and shipping production‑grade AI/LLM systems such as RAG, agent workflows, tool‑calling systems, AI APIs, or LLM‑powered product capabilities.
• Strong programming experience in Python or backend service stacks used for production APIs and distributed systems.
• Deep understanding of API design, service boundaries, SDKs, integration patterns, reliability, testing, observability, performance, and cost optimization.
• Practical experience with LLM application architecture (context engineering, retrieval patterns, tool use, structured outputs, orchestration, fallback handling, evaluation).
• Ability to build evaluation and validation systems for AI applications.
• Experience deploying and operating cloud‑based production systems on AWS, GCP, Azure, or similar.
• Strong technical judgment and ability to make trade‑offs across speed, reliability, safety, cost, developer experience, and business impact.
• Ability to lead through hands‑on technical contribution: writing code, creating reference implementations, reviewing designs, mentoring engineers.
• Proven ability to influence across teams via architecture reviews, design documents, standards, mentorship, and partnership.