AI EngineerAI

What You Bring

Required

• 4+ years building production backend/platform systems — distributed services, APIs, async processing, and systems that serve multiple consumers. Python primary; experience with high-throughput, low-latency services.
• Deep hands-on experience with LLMs in production — not just using them, but building the infrastructure around them: orchestration, tool calling, retrieval pipelines, context management, prompt chaining, and multi-agent coordination. Proficiency with LangChain, LangGraph, or equivalent orchestration frameworks.
• Experience building platform primitives — registries, gateways, evaluation frameworks, tracing systems, or similar shared infrastructure that other teams build on top of. You understand contracts, versioning, and what it means to ship a platform rather than a feature.
• Demonstrated ability to make build vs. adopt decisions — you have integrated open-source tooling into production systems, understood its boundaries, and built the proprietary layer where needed.
• Experience with RAG systems at depth — indexing strategies, retrieval evaluation, chunking, re-ranking, hybrid search, and the failure modes (context pollution, retrieval misses, conflicting sources). Familiarity with vector databases (Pinecone, Weaviate, Qdrant, or similar) and embedding models.
• Familiarity with real-time system constraints — you understand how latency budgets, deadlines, and streaming shape architecture differently than batch systems.
• Strong systems design skills — you can design a registry, a gateway, a memory service, or an evaluation harness that serves multiple consumers with different needs (internal teams and external customers, real-time and offline paths).

Strongly Preferred

• Experience with evaluation of LLM systems — designing metrics, building test harnesses, measuring behavioral consistency, detecting regressions. Familiarity with DeepEval, Ragas, or similar frameworks.
• Experience with multi-tenant platforms — tenant isolation, scoped configuration, and the engineering discipline required when one customer's data/behavior must never leak to another.
• Background in speech/multimodal AI pipelines — ASR, TTS, avatar rendering, or video generation pipelines. Understanding how these compose with a reasoning layer.
• Experience with observability and tracing at the platform level — structured per-decision traces, not just application logs. Familiarity with Langfuse, Opik, OpenTelemetry, or similar.
• Familiarity with agentic AI frameworks and patterns — CrewAI, AutoGen, Semantic Kernel, MCP protocol, and the failure modes unique to multi-step AI systems (planning loops, tool selection, delegation, parallel execution).
• Experience with guardrails and safety for AI systems — input filtering, output validation, grounding checks, and the latency trade-offs of validation in real-time streaming.

What Success Looks Like

1 month: Deep understanding of the current architecture (Brain, Conversation Manager, real-time and offline pipelines). First contribution shipped — either to evaluation/tracing adoption or registry infrastructure.

3 months: One major platform component owned and in production — registry, evaluation harness, guardrails, or gateway. Integrated with the real-time pipeline and validated under latency constraints. FDE teams consuming your work.

6 months: Multiple foundation components live and serving both runtimes. Evaluation gates enforced on registry entries. Tracing producing actionable diagnostics. Your components are what make new customer onboardings faster rather than bespoke.

Why This Role

You will build the platform layer that turns proven AI technology into an enterprise-grade system. The engine exists — real-time avatar conversations, low-latency speech, offline video generation. What doesn't exist yet is the shared infrastructure that makes it extensible, measurable, and reliable across many customers. That's what you build.

Every component you ship is consumed by multiple teams and multiple customers. Your work compounds.