Interesting Job Opportunity: Auxo AI - Senior Applied AI Engineer - AI Agents

Auxo AI; Gurugram District; On-site; INR 4,000,000 - 6,500,000; Full time

Job summary

AuxoAI in Gurugram, Haryana, seeks a Senior Applied AI Engineer to design and deploy production-grade AI agents capable of structured reasoning, planning, and decision-making. You will build intelligent agent systems that fuse LLM-based reasoning with classical planning, search algorithms, and optimization techniques.

You will implement modular agent architectures, validate tool-calling, and create evaluation frameworks to measure reliability, cost efficiency, and latency under real-world

Qualifications

• Strong Python engineering skills.
• Experience building production-grade AI agent systems.
• Experience with planning/decision-making algorithms and agent architectures.

Responsibilities

• Design and architect modular AI agent frameworks with skill decomposition, tool orchestration, and persistent state tracking.
• Implement planning and search algorithms (MCTS, beam search, A*, heuristic search) for complex decisions.
• Develop decision loops balancing exploration/exploitation, cost vs. accuracy, latency vs. reasoning depth.
• Build memory systems (episodic/semantic/vector) with efficient retrieval.
• Design tool-calling architectures with robust execution validation and retry/failure recovery.
• Develop evaluation frameworks using task success metrics and rollout simulations.
• Improve agent performance with distillation, synthetic trajectories, and prompt optimization.
• Deliver production-ready agent systems meeting reliability, cost, throughput requirements.

Skills

Python

MCTS

Planning

Agent frameworks

Graph algorithms

Reinforcement learning

System design

Education

BS/MS in CS/Math/ML

Tools

Monte Carlo Tree Search

A* search

LLM tooling

Job description

Description

AuxoAI is hiring a Senior Applied AI Engineer to design and deploy production-grade AI agents capable of structured reasoning, planning, and decision-making.

Description

AuxoAI is hiring a Senior Applied AI Engineer to design and deploy production-grade AI agents capable of structured reasoning, planning, and decision-making.

This role focuses on building intelligent agent systems that combine LLM-based reasoning with classical planning, search algorithms, and optimization techniques. The ideal candidate will develop robust agent architectures that operate reliably in real-world environments with constraints around latency, cost, uncertainty, and limited context windows.

You will work on advanced AI systems that power autonomous workflows, decision engines, and tool-driven agent ecosystems.

You will work on problems where existing architectures may not be sufficient, and will be expected to experiment with new approaches that combine machine learning, graph algorithms, and classical AI techniques to build reliable, production-grade systems.

Responsibilities

• 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 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.

Nice To Have

• Experience with reinforcement learning techniques such as policy gradients, value estimation, or reward modeling.
• Experience building multi-agent or collaborative agent systems.
• Experience designing evaluation frameworks for agent robustness and reliability.
• Experience optimizing LLM inference pipelines for latency, throughput, and cost efficiency.
• Familiarity with distributed task orchestration systems and large-scale AI workflow management.

(ref:hirist.tech)