AI Engineer

Job Description

AuxoAI is hiring a 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.

Requirements

• 3–10 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
— including feature engineering, cross-validation, and model monitoring in
production.

• 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, including model deployment,
endpoint management, and AI pipeline orchestration.

• Experience integrating AI solutions with
enterprise data systems — ERP APIs, data lakehouses (Databricks), or industrial
data sources (MES, IoT/sensor streams).

• Strong understanding of RAG architectures,
vector databases, and retrieval strategies — with the ability to go beyond
retrieval into agentic reasoning and action.

• Familiarity with real-time or streaming data
processing patterns (Pub/Sub, Kafka, or equivalent) for inference on live
operational data.

• Strong Python engineering skills with a focus
on scalable, reliable, and maintainable system design.

Candidates whose primary experience is limited
to RAG pipelines or prompt engineering without hands-on ML model development or
production agent delivery may not be a strong fit for this role.

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.