Senior AI Engineer

Role Description

This is a remote position. We're building agentic AI systems that autonomously reason, plan, and act across complex workflows. You'll be a core contributor designing and shipping production-grade AI agents, integrating with LLM providers, orchestrating multi-step workflows, and building the infrastructure that makes agents reliable, observable, and scalable. This is a deeply technical role — you'll work at the intersection of LLM engineering, backend systems, and agent architecture.

Qualifications

• Deep experience with at least one major LLM provider SDK (Anthropic, OpenAI, Gemini)
• Prompt engineering depth: few-shot design, chain-of-thought, structured outputs, tool/function calling, multi-turn context management, prompt caching, token budgeting
• Built real production agents — not just chatbots; systems that take actions, use tools, and operate autonomously
• Understands agent patterns: ReAct, plan-and-execute, multi-agent orchestration, reflection loops, supervisor/worker hierarchies
• Familiar with MCP (Model Context Protocol), A2A, and agent frameworks (LangGraph, CrewAI, AutoGen) — and when NOT to use them
• Knows how to handle non-determinism: retry strategies, output validation, graceful degradation

Requirements

• Design and build AI agents — autonomous systems that use tools, browse the web, call APIs, manage memory, and complete multi-step tasks with minimal human intervention
• Architect agentic workflows — orchestrate multi-agent pipelines (planner → executor → verifier patterns), handle tool-use loops, HITL checkpoints, retries, and failure recovery
• Integrate LLM providers — work with Anthropic Claude, OpenAI, Gemini; implement prompt caching, structured outputs, and context management at scale; select the right model for the right task
• Build knowledge pipelines — design RAG systems, hybrid retrieval, vector stores, embedding pipelines, and memory layers that give agents contextual awareness
• Own backend reliability — REST/GraphQL APIs, async job queues, observability, latency optimization, cost tracking for token usage
• Drive system design decisions — lead architecture discussions, define component boundaries, evaluate build vs. buy tradeoffs, and document decisions (ADRs, diagrams)
• Maintain CI/CD pipelines — keep deployments fast, safe, and automated across dev/staging/prod environments
• Evaluate agent behavior — build LLM tracing, regression frameworks, and evals pipelines to detect behavioral drift, intent failures, and quality degradation across model updates
• Stay ahead of the curve — evaluate emerging standards (MCP, A2A, reasoning models, new frameworks) and make adoption recommendations

Benefits

• You'll work on AI problems that are genuinely unsolved — not CRUD apps with an LLM wrapper
• Greenfield architecture decisions — your opinions shape the system
• Direct impact on product; small team, high ownership
• Exposure to the full agentic stack from infra to eval to UX

Company Description

General company description goes here.