Artificial Intelligence Engineer

A fast-growing AI-native software company is looking for a strong AI Engineer to build the core infrastructure behind its agentic AI platform. This role is focused on the shared platform layer that enables AI agents to be extensible, reliable, measurable, and scalable across multiple enterprise customers and use cases.

You will work on the foundational systems that allow customer-facing teams to build and deploy agentic AI capabilities without bespoke engineering for every customer. This includes orchestration, skills infrastructure, evaluation, guardrails, memory, integrations, retrieval, and observability.

This is a hands-on engineering role for someone who enjoys building platform primitives that other technical teams rely on, and who wants to work at the center of modern LLM infrastructure, multi-agent systems, and enterprise-grade AI deployment.

Responsibilities

• Build and scale core backend infrastructure for an agentic AI platform.
• Design and develop shared platform components such as orchestration services, registries, gateways, evaluation systems, tracing, memory, and retrieval infrastructure.
• Work on LLM infrastructure including tool calling, context management, RAG pipelines, prompt chaining, and multi-agent coordination.
• Create reliable platform services that can support multiple internal teams, enterprise customers, and AI runtime environments.
• Evaluate and integrate open-source AI tooling where appropriate, while building proprietary infrastructure where differentiation or scalability is required.
• Design systems that support versioning, tenant isolation, scoped configuration, observability, and platform-grade reliability.
• Partner with product, engineering, and customer-facing teams to turn recurring customer needs into reusable platform capabilities.
• Ensure systems are built with performance, latency, reliability, and safety considerations in mind.

Requirements

• 4+ years of experience building production backend or platform systems, including distributed services, APIs, async processing, and multi-consumer infrastructure.
• Strong Python experience, with exposure to high-throughput or low-latency backend environments.
• Hands-on production experience with LLM systems, including orchestration, tool/function calling, retrieval pipelines, context management, and multi-agent workflows.
• Experience with agent frameworks such as LangChain, LangGraph, Semantic Kernel, CrewAI, AutoGen, or similar.
• Experience building platform primitives such as registries, gateways, evaluation tools, tracing systems, memory services, or shared infrastructure used by other engineering teams.
• Strong understanding of RAG systems, including indexing, retrieval evaluation, chunking, re-ranking, hybrid search, and common failure modes.
• Familiarity with vector databases such as Pinecone, Weaviate, Qdrant, or similar.
• Strong systems design skills and the ability to build scalable, reusable infrastructure rather than one-off features.
• Ability to make practical build-versus-adopt decisions when working with open-source and commercial AI tooling.

Preferred Qualifications

• Experience building evaluation frameworks for LLM or agentic systems, including metrics, test harnesses, regression detection, and quality measurement.
• Familiarity with tools such as DeepEval, Ragas, Langfuse, Opik, OpenTelemetry, or similar.
• Experience with multi-tenant SaaS platforms, including tenant isolation, scoped configuration, and data separation.
• Background in conversational AI, speech, multimodal AI, or real-time AI systems.
• Experience with guardrails, safety mechanisms, input filtering, output validation, and grounding checks.
• Understanding of latency-sensitive architectures, especially in real-time or streaming environments.
• Experience creating infrastructure that enables multiple teams or customers to build on top of a shared platform.

What Makes This Opportunity Exciting

• Build the core infrastructure behind a next-generation agentic AI platform.
• Work on complex problems across LLM orchestration, evaluation, RAG, integrations, tracing, and multi-agent systems.
• Create reusable platform components that directly accelerate customer deployments.
• Join a highly technical environment where your work becomes the foundation for multiple products, teams, and enterprise use cases.
• Help turn advanced AI capabilities into scalable, production-grade enterprise infrastructure.