AI Agent Developer
14 hours ago
Job description
Project Overview:
We are looking for a highly skilled AI Engineer (Master's or PhD background preferred) to build a robust, autonomous AI agent.
We need an applied engineer who understands machine learning, can orchestrate Large Language Models (LLMs) for complex reasoning, and knows how to implement feedback loops (using applied Reinforcement Learning concepts) so the agent can self-correct and improve its performance over time.
If you have a strong academic foundation in AI/ML but love getting your hands dirty building practical, working systems, this is the perfect role for you.
The Challenge:
Current LLMs are great at generating text, but they struggle with multi-step execution without drifting off track.
Your objective is to build an agentic workflow that can break down a goal, use external tools to gather data, evaluate its own progress, and adjust its approach based on environment feedback.
Core Responsibilities:
- Agent Architecture: Develop an AI agent capable of multistep planning, task execution, and utilizing external tools (APIs, web search, databases).
- LLM Orchestration: Build robust workflows using stateoftheart models (GPT4, Claude 3.5, or opensource equivalents) and frameworks like LangGraph, AutoGen, or CrewAI.
- Feedback Loops & Applied RL: Implement mechanisms (such as selfreflection loops, reward formulations, or basic applied RL) to help the agent learn from successes and failures, minimizing hallucinations and errors over time.
- RAG Implementation: Build reliable Retrieval-Augmented Generation (RAG) pipelines to give the agent accurate, uptodate context from our datasets.
- System Integration: Write clean, modular Python code to connect the agent to our existing backend infrastructure and APIs.
Required Technical Stack & Expertise:
- Academic/
Practical Background:
Master's or PhD in Computer Science, Data Science, AI, or equivalent highly technical, hands-on experience.
- Machine Learning Foundation: Solid understanding of ML fundamentals, neural networks, and proficiency in Python (PyTorch, TensorFlow, or Scikitlearn).
- LLM Engineering: Proven experience with prompt engineering, contextwindow management, and agent frameworks.
- Reinforcement Learning Concepts: You don't need to be a theoretical RL researcher, but you must understand how to apply RL principles or programmatic reward structures to optimize an agent's decisionmaking.
- Software Engineering: Strong ability to write clean, documented, productionready code.
Who You Are:
- You understand the math and theory behind modern AI, but your primary focus is on building things that actually work.
- You know how to troubleshoot an LLM when it gets stuck in a loop.
- You are highly communicative and can explain complex AI concepts in practical terms.
Mandatory skills:
Machine Learning, applied reinforcement learning, LLM engineering, Agent Architecture, LLM Orchestration, Feedback Loops & Applied RL, RAG, AI Agent Development
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