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.