Principal AI Engineer

Accountabilities: • Design and develop orchestration and abstraction layers connecting LLMs with tools, data, and specialized sub-agents.

• Build and operate MCP servers while establishing standards for defining, exposing, and versioning tools.

• Define tool-surface strategies, including the appropriate number of tools and APIs exposed to LLMs and how they remain coherent and discoverable.

• Establish when to use specialized sub-agents versus direct tool exposure and develop corresponding multi-agent patterns.

• Design and optimize RAG systems covering chunking, embeddings, vector stores, hybrid search, re-ranking, and context assembly.

• Create abstraction layers that decouple product teams from underlying models, tools, and providers.

• Develop routing, memory, and context-window management strategies for agentic workflows.

• Define evaluation frameworks covering retrieval quality, tool-selection accuracy, task success, latency, and cost.

• Establish observability and tracing across multi-step agent and tool interactions.

• Implement safety measures, guardrails, authentication, and access controls across AI tools and agents.

• Collaborate with product teams to onboard new capabilities as tools and agents into the central AI system.

• Mentor engineers and help raise orchestration, retrieval, and agentic AI engineering standards across teams.

Requirements:

• Advanced English proficiency at C1/C2 level for fluent communication across distributed teams.

• 10+ years of experience in software or AI engineering, including hands-on development of LLM orchestration, agents, and retrieval systems.

• 4–5+ years of deep hands-on experience with LLM orchestration frameworks such as LangGraph, LlamaIndex, Semantic Kernel, or equivalent technologies.

• Direct experience building MCP servers and implementing tool or function-calling integrations.

• Evidence-based expertise in designing effective LLM tool surfaces, including decisions around the number of tools and APIs exposed through MCP servers.

• Strong understanding of the tradeoffs between specialized sub-agents and direct tool exposure, with experience designing multi-agent architectures.

• Deep expertise in retrieval and RAG, including chunking strategies, embeddings, vector databases, hybrid or keyword search, and re-ranking.

• Experience designing abstraction layers and platform APIs used by multiple engineering teams.

• Strong knowledge of context-window management, prompt and context assembly, and AI cost and latency optimization.

• Experience implementing evaluation, monitoring, observability, and tracing for agentic and retrieval systems.

• Ability to establish technical strategy and standards while remaining actively involved in coding and architecture.

• Strong stakeholder management, collaboration, communication, and problem-solving skills.

Benefits:

• Competitive salary paid in USD.

• 100% remote work.

• Independent Contractor agreement.

• Long-term contract.

• 2 weeks of paid time off (PTO).

• Holidays based on the North American calendar.

• Full-time working hours aligned with EST.

• Fully committed full-time engagement.

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