Senior AI Engineer - AI Agent Systems

AuxoAI is hiring a Senior 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 and predictive ML solutions that power real-world enterprise workflows — going well beyond chatbot or RAG-style application development. The ideal candidate will design AI architectures that combine LLM-based reasoning with classical ML techniques, operating reliably in production environments with constraints around latency, cost, data quality, and enterprise system integration.

You will work on advanced AI systems that power autonomous workflows, decision engines, and tool-driven agent ecosystems — spanning use cases in manufacturing, finance, supply chain, and enterprise operations.

You will also work on problems where existing architectures may not be sufficient and will be expected to experiment with new approaches that combine large language models, machine learning models, and data engineering patterns to build reliable, production-grade systems

Responsibilities:

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

• Build and deploy supervised and unsupervised ML models for prediction, classification, anomaly detection, and pattern recognition tasks in production environments.
  

• 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 optimised retrieval strategies.
  

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

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

• Integrate AI agents and ML models with enterprise systems.
  

• Deliver production-ready AI systems that meet operational requirements around reliability, cost efficiency, throughput, observability, and enterprise security standards.