AI / Machine Learning Engineer - Autonomous LLM Systems
Join us in revolutionizing the field of Agentic AI at a groundbreaking organization dedicated to developing systems where large language models (LLMs) can reason, act, and collaborate independently to achieve tangible results.
As a crucial member of our team, you'll design innovative frameworks, tools, and data loops that empower intelligent agents to think, plan, and enhance their own performance—turning raw model capabilities into high-performance autonomous AI systems.
Key Responsibilities:
• Develop Autonomous Systems - Construct and refine LLM-based agents capable of independent reasoning, planning, and executing complex, multi-step tasks.
• Improve LLM Reasoning - Utilize reinforcement learning, tool usage, and reflection techniques to enhance decision-making skills and contextual awareness.
• Create Scalable Frameworks - Develop data pipelines, annotation tools, and feedback loops that speed up model iteration and evaluation processes.
• Collaborate with Research Teams - Transform experimental insights into production-ready systems that boost the autonomy and dependability of AI agents.
• Manage End-to-End Experiments - Take ownership of your computational environments (e.g., Jupyter, Colab, Databricks) and iteratively work on large-scale LLM training and assessment.
What We Seek:
• 4+ years of experience in Machine Learning or AI, particularly with LLM agent systems, tool-use frameworks, or generative AI.
• Expertise in Python and ML frameworks like PyTorch or TensorFlow.
• Practical experience in developing or fine-tuning models such as LLaMA or GPT.
• Solid understanding of agentic architectures, chain-of-thought reasoning, and memory/reflection techniques.
• Proven track record in debugging, optimizing, and scaling machine learning experiments and computing pipelines.
• MSc or PhD in AI, Computer Science, or a related field.
We invite you to apply for immediate consideration and become a part of our innovative journey!
