AI Engineer
RemoteUnited States
Job Summary
Design, develop, and deploy sophisticated multi-agent AI systems leveraging Chain of Thought, Self-Consistency Decoding, and Reflexion for robust decision-making. Implement tree-based algorithms including depth-first traversal and AVL rebalancing to integrate AI outputs into production solutions. Build high-performance infrastructure utilizing Graph of Thoughts to structure non-linear reasoning graphs and sustain a modular computational framework for scalable applications. Collaborate with DevOps teams to implement MLOps pipelines using Docker, Kubernetes, and Temporal, while working with product and engineering teams to define technical vision.
Required Qualifications
- 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.
Desired Qualifications
- 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.
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