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About The Role
AuxoAI is hiring AI Engineers 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.
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.
Requirements
- 4 - 10 years of experience building machine learning or AI systems in production environments.
- Hands-on experience training, evaluating, and deploying ML models using frameworks such as scikit-learn, XGBoost, or PyTorch.
- Strong experience building or extensively customising agent frameworks for real-world applications.
- Hands-on experience designing tool-use or function-calling architectures under practical system constraints.
- Experience working with cloud-native AI platforms, preferably GCP Vertex AI and Gemini.
- Experience integrating AI solutions with enterprise data systems - ERP APIs, data lakehouses (Databricks), or industrial data sources.
- Strong understanding of RAG architectures, vector databases, and retrieval strategies.
- Familiarity with real-time or streaming data processing patterns (Pub/Sub, Kafka, or equivalent).
- Strong Python engineering skills with a focus on scalable, reliable, and maintainable 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 optimising LLM inference pipelines for latency, throughput, and cost efficiency.
- Familiarity with MLOps practices including model versioning, drift monitoring, retraining pipelines, and model registries.
- Familiarity with distributed task orchestration systems and large-scale AI workflow management.
- Prior experience in semiconductor, manufacturing, or industrial AI environments.
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