AI Engineer

Requirements

• Proficiency in Python programming.
• Extensive experience with development and deployment tools, including containerization (e.g., Docker, Kubernetes) and workflow orchestration (e.g., Temporal or similar).
• Hands-on expertise in applied AI engineering, with a focus on multi-agent systems incorporating advanced reasoning capabilities.
• Strong proficiency in advanced algorithms, particularly tree traversal, rebalancing, and navigation over tree structures.
• Demonstrated experience with prompt engineering to maximize the capabilities of large language models (LLMs).
• Proficiency with agentic workflow tools (e.g., Pydantic, LangGraph, or similar) to support reasoning frameworks like Graph of Thoughts.
• Experience developing deep learning models using frameworks such as PyTorch or TensorFlow, with an emphasis on reasoning-intensive applications.
• Practical experience fine-tuning foundation models to enhance reasoning capabilities, including the integration of Self-Consistency Decoding.
• Familiarity with MLOps practices and tools (e.g., MLflow or similar) to streamline the deployment of reasoning-focused multi-agent systems.
• Experience with applying Graph of Thoughts for non-linear reasoning and iterative reasoning techniques, such as Reflexion to refine agent decision-making through self-evaluation and feedback mechanisms.
• Understanding of ReAct for dynamic decision-making.