📍 Location: Serbia, Armenia (We are ready to discuss other countries as well)

🏢 Remote work is possible

💶 Payment terms are open to discussion from 3500 € and up

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About the product

At FlameTree, we are building a platform for creating AI agents that help businesses scale customer support, lead follow-up, and sales across multiple communication channels — both inbound and outbound.

Our AI agents work with knowledge bases, communicate in real time, and drive conversions across messaging platforms. The platform supports 150+ languages and integrates with WhatsApp, email, and web applications, offering strong security and high scalability for business growth.

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Responsibilities:

— Design and develop the core agent layer responsible for orchestrating interactions with LLMs

— Build and maintain complex conversational logic: state machines, agent workflows, and orchestration pipelines

— Control and shape LLM behavior: prompt design, structured outputs, deterministic flows

— Manage conversational context: memory, history, token limits, and degradation strategies

— Ensure reliability and predictability on top of inherently non-deterministic models

— Implement resilient integrations with LLM providers (timeouts, retries, fallbacks, multi-provider strategies)

— Optimize latency and cost (streaming, batching, caching, token efficiency)

— Debug complex production issues (inconsistent outputs, race conditions, state loss)

— Contribute to system architecture: clear boundaries between agents, backend, and real-time components

— Build observability around LLM pipelines (prompt/response logging, tracing, quality metrics)

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Requirements:

— 5+ years of backend development experience with strong Python skills (async, architecture, performance)

— Proven production experience with LLMs (not side projects): understanding of limitations, cost, and behavior

— Experience building agent-based systems or complex orchestration logic (state machines, pipelines)

— Ability to make LLM behavior predictable (structured outputs, schema validation, guardrails)

— Strong debugging skills in non-deterministic systems

— Deep understanding of API integrations (timeouts, retries, idempotency, backpressure)

— Experience optimizing latency and throughput in production systems

— Solid Docker experience and understanding of production environments

— Ability to make architectural decisions independently and take ownership

— Strong engineering mindset: writing maintainable, scalable, production-grade code

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Nice to Have:

— Experience with multi-agent systems, tool/function calling

— Experience with local LLMs (Ollama, vLLM, GPU inference)

— Experience with real-time / voice systems

— LLM observability (prompt tracing, evals, quality metrics)

— Cost optimization at scale for LLM usage

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What Makes This Role Interesting:

— You will work on the core intelligence layer of the product — not just integrations

— Real production challenges: high load, low latency, reliability requirements

— Direct impact on system architecture and technical decisions

— Fast execution cycle — minimal bureaucracy

— Engineering-driven approach to LLMs (reliability, control, metrics — not just prompt tinkering)

— Strong engineering team focused on building real systems, not prototypes

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Who This Role Is NOT For:

— Candidates without real production experience with LLMs

— Engineers relying only on frameworks without understanding underlying mechanics

— Developers without experience in high-load or latency-sensitive systems

— People focused on quick hacks rather than building reliable systems

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📩 If you want to join a team where everything is fast, exciting, and truly about AI — drop a message