Artificial Intelligence Engineer

Role Overview

AI Agent Architecture

• Architect and implement single-agent and multi-agent systems.
• Design multi-step workflows and orchestration patterns, including planner-executor, supervisor-worker, routing, delegation, and hierarchical agents.
• Build reusable tools, skills, memory, policies, and guardrails while balancing autonomy, reliability, cost, and performance.

Workflows and Integrations

• Build stateful workflows with LangGraph, LangChain, or equivalent frameworks.
• Implement structured outputs, deterministic validation, event-driven triggers, asynchronous processing, retries, idempotency, checkpointing, timeouts, and recovery.
• Develop custom tools and securely integrate agents with APIs, applications, databases, approved data sources, and MCP.

RAG, Memory, and Safety

• Design production-grade RAG pipelines using embeddings, vector search, hybrid retrieval, metadata filtering, and reranking.
• Develop short-term and long-term memory plus context management, retrieval, summarization, and persistence strategies.
• Implement guardrails, permissions, human approval checkpoints, prompt-injection protection, and safe failure behavior.

Evaluation and Optimization

• Build automated evaluations for task completion, groundedness, tool selection, safety, reliability, and agent decision quality.
• Implement tracing and observability for tool calls, latency, failures, token consumption, and operational costs.
• Optimize token consumption, model selection, latency, execution efficiency, usage metering, and scalability.

Required Qualifications

• Production experience building and deploying agentic AI systems is required.
• Experience with agent orchestration, tool calling, structured generation, multi-step execution, custom tools, external APIs, messaging technologies, RAG, embeddings, vector search, and agent memory is required.
• Experience with guardrails, approval workflows, permission-controlled tool execution, evaluation, tracing, and observability is required.
• Strong proficiency in Python, TypeScript/Node.js, or both is required.
• Strong software engineering fundamentals and experience operating AI systems in production are required.

Additional Experience

• Strong candidates may have experience with production agentic AI platforms, event-driven architectures, MCP, AI security, cost optimization, and vector databases.
• Standout experience includes autonomous business workflows, multi-agent systems at scale, shared AI infrastructure, open-source contributions, and taking systems from prototype to production to scale.

Role Scope