Research Engineer (AI)

TL;DR

Research Engineer (AI): Designing, building, and productionizing multi-agent platforms that leverage large language models (LLMs) to automate complex enterprise workflows with an accent on autonomous reasoning, decision-making, and action across enterprise environments. Focus on translating cutting-edge LLM and agent research into production-ready platform capabilities and embedding agentic workflows into customer-facing solutions.

Location: Remote (Mexico)

Company

hirify.global provides cloud analytics and data platform for AI.

What you will do

• Architect and implement multi-agent systems that coordinate specialized LLM-powered agents to solve complex analytical and operational tasks.
• Design agent orchestration patterns including task decomposition, inter-agent communication, tool use, memory management, and feedback loops.
• Build scalable agentic pipelines that integrate with hirify.global’s data platform, enabling agents to query, analyze, and act on enterprise data autonomously.
• Design and manage LLM inference pipelines optimized for latency, throughput, and cost across cloud and on-premises deployments.
• Build and extend internal agentic SDKs and frameworks to rapidly develop and deploy agent-based applications.
• Collaborate with research teams to translate cutting-edge LLM and agent research into production-ready platform capabilities.

Requirements

• Deep hands-on expertise with large language model APIs and inference frameworks (e.g., OpenAI, Anthropic, Mistral, vLLM, Ollama, HuggingFace Transformers).
• Strong practical experience designing and building multi-agent systems using agentic SDKs and orchestration frameworks such as LangChain, LlamaIndex, AutoGen, CrewAI, or equivalent.
• Proficiency in Python and experience building production-grade AI/ML services with clean, well-documented, testable code.
• Solid understanding of RAG architectures, vector databases (e.g., Pinecone, Weaviate, pgvector), and knowledge retrieval patterns.
• Experience with prompt engineering, LLM evaluation methodologies, and strategies for improving agent reliability and reducing hallucination.
• 5+ years of software engineering experience, with at least 2 years focused on LLM-based systems, agentic workflows, or applied AI research.

Nice to have

• Experience building or contributing to agentic SDK frameworks or open-source LLM tooling.
• Familiarity with Model Context Protocol (MCP) or similar standards for tool-augmented LLM systems.
• Background with reinforcement learning from human feedback (RLHF), fine-tuning, or model alignment techniques.

Culture & Benefits

• People-first culture.
• Flexible work model.
• Focus on well-being.
• Inclusive environment.