استخدام AI Engineer

شرح موقعیت شغلی

• Own the agent loop. Improve how the agent plans, calls tools, recovers from errors, manages context, and decides when to stop. Today the loop is a competent but vanilla tool-call loop — there's a lot of room to make it smarter, faster, and cheaper.
• Tune the prompts. System prompts, tool descriptions, memory-retrieval prompts, planning prompts, and delegation prompts all need iteration. You'll own the prompt library and the discipline that keeps it from rotting.
• Design the model router. Cheap models for trivial turns, strong models for hard reasoning, vision models when needed, local models when privacy demands it. Each agent has a primary and a fallback. You'll design and ship a smarter routing layer.
• Improve tool calling. Tool registry, tool descriptions, argument schemas, error feedback, result shaping — all affect agent reliability. You'll make tools more reliable and tool errors more recoverable.
• Make multi-agent delegation work. One agent handing a task to another sounds simple — it isn't. Constraint passing, context scoping, result aggregation, deadlock avoidance, budget inheritance. You'll design the delegation protocol and prove it works on real tasks.
• Build the evaluation harness. Without evals, we're guessing. You'll build a test suite of real agent tasks (with golden outputs, automated graders, regression tracking) so every prompt change, model swap, or runtime tweak gets measured before it ships.
• Wrestle with memory. Retrieval quality is a major lever on agent performance. You'll improve how memory is written, summarized, indexed, and surfaced into prompts.
• Handle the unglamorous parts. Token budgets, cost tracking, prompt caching, context compaction, latency, retries, streaming. The platform succeeds or fails on these.

• 3+ years engineering experience, with at least 1+ year shipping LLM-powered products (not just prototypes — production systems with real users)
• Deep familiarity with the Anthropic, OpenAI, or Google Gemini SDKs — you've written real tool-use loops, not just chat completions
• Strong TypeScript / Node.js. You can read and modify a production codebase without hand-holding
• Genuine prompt-engineering instinct. You've debugged "why is the model doing X" and can articulate what you'd try and why
• Experience with agent frameworks or building one — you understand tool-calling loops, ReAct, planning, context management
• Comfortable with eval design. You don't ship LLM changes without measuring them
• Pragmatic about model selection. You know when to use the big model and when not to

• Built or contributed to a multi-agent system (not just chained prompts)
• Experience with MCP (Model Context Protocol) tooling
• Familiarity with prompt caching, structured outputs, vision models, long-context handling
• Worked with local/open-weight models (vLLM, Ollama, llama.cpp)
• Track record reducing LLM cost and latency on a real product
• Built RAG / memory systems with real-world retrieval quality discipline (not just cosine_similarity > 0.7)
• Experience with browser automation agents, computer-use agents, or code-execution agents
• Comfortable doing the "writer" parts of prompt engineering — you can write clear English instructions, not just engineer the scaffolding

• You've published evaluations, prompts, or model comparisons others have used
• You've shipped agentic features that materially changed product metrics
• You've trained or fine-tuned models, even if you don't claim to be an ML researcher

• Competitive salary.
• Opportunity to work with cutting-edge AI technology.
• A collaborative and inclusive work environment.

مهارت‌های مورد نیاز

• Ai
• OpenAI

حداقل سابقه کار

• سه تا شش سال

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