Senior Applied AI Engineer

- Design and architect modular AI agent frameworks incorporating skill decomposition, tool orchestration, and persistent state tracking.

- Implement planning and search algorithms such as Monte Carlo Tree Search (MCTS), beam search, A search, heuristic search, and graph-based planning approaches to support complex decision-making tasks.

- Develop decision-making loops that balance trade-offs between exploration vs. exploitation, cost vs. accuracy, and latency vs. reasoning depth.

- Build structured memory systems including episodic memory stores, semantic memory layers, and vector-based memory with optimized retrieval strategies.

- Design tool-calling architectures with strong execution validation, retry mechanisms, and failure recovery strategies.

- Develop evaluation frameworks to measure agent performance using task success metrics, rollout simulations, and multi-sample validation approaches.

- Improve agent performance through techniques such as distillation, synthetic trajectory generation, prompt compression, and context pruning.

- Deliver production-ready agent systems that meet operational requirements around reliability, cost efficiency, throughput, and observability.

Requirements:

- Strong experience implementing search or planning algorithms beyond basic use cases, including tree search or heuristic-based planning approaches.

- Hands-on experience with Monte Carlo Tree Search (MCTS) or related decision-making frameworks.

- Strong understanding of state-space representations, heuristic design, and decision boundary trade-offs.

- Experience building or extensively customizing agent frameworks for real-world applications.

- Hands-on experience designing tool-use or function-calling architectures under practical system constraints.

- Strong Python engineering skills with a focus on scalable and reliable system design.