AIOps, LLMOps, MLOps (Machine Learning Operations) DevOps SRE Engineer

Avensys Consulting; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Avensys Consulting in Singapore seeks a specialist to implement automated AI/ML pipelines, observability, and model lifecycle automation across training, deployment, and evaluation. You will drive AIOps-based reliability, trigger retraining, and guardrail enforcement for AI and LLM operations, while monitoring health, latency, drift, and GPU utilization.

You’ll collaborate with safety and security teams to enforce guardrails, version control, audit readiness, and governance in CI/CD workflows

Qualifications

• Hands-on experience in AI/ML pipeline orchestration and model lifecycle automation.
• Strong knowledge of MLOps, LLMOps, and AIOps for CI/CD and model retraining.
• Experience with AI observability, monitoring, and telemetry for AI systems.

Responsibilities

• Hands-on experience in AI/ML pipeline orchestration (Kubeflow, MLflow, Airflow, Azure ML) and model lifecycle automation.
• Deep understanding of MLOps, LLMOps, and AIOps frameworks for CI/CD, model retraining, evaluation, and deployment at scale.
• Proficient in observability and telemetry integration for AI systems — monitoring model health, drift, inference latency, and GPU utilization.
• Skilled in automated incident detection, correlation, and remediation using AI-driven observability and alerting systems.
• Knowledge of data governance, evaluation frameworks (Promptfoo, Portkey, RAG validation) and DevSecOps principles for AI pipelines.
• Design and operationalize the AI reliability stack, covering training, inference, and evaluation pipelines across Central Kitchen shared services.
• Implement automated health checks, anomaly detection, and retraining triggers for deployed LLM and RAG models.
• Collaborate with AI Safety and Security teams to enforce model guardrails, version control, and change governance in CI/CD workflows.
• Establish monitoring baselines and SLOs for AI workloads (throughput, latency, accuracy, drift) across all environments.
• Integrate AIOps-driven diagnostics to reduce mean time to detect (MTTD) and mean time to recover (MTTR) in AI platform operations.
• Support SRB/ARB reviews by maintaining clear operational documentation, evaluation evidence, and audit readiness of AI components

Skills

AI/ML pipelines

MLOps

CI/CD

Kubeflow

MLflow

Airflow

Azure ML

Tools

Kubeflow

MLflow

Airflow

Azure ML

Job description

Avensys is a reputed global IT professional services company headquartered in Singapore. Our service spectrum includes enterprise solution consulting, business intelligence, business process automation and managed services. Given our decade of success, we have evolved to become one of the top trusted providers in Singapore and service a client base across banking and financial services, insurance, information technology, healthcare, retail and supply chain,

Job Summary

Implements automated pipelines and observability for AI model training, deployment, and evaluation.

Drives AIOps-based reliability, model retraining triggers, and guardrail enforcement for AI and LLM operations.

Responsibilities | CORE

• Hands-on experience in AI/ML pipeline orchestration (Kubeflow, MLflow, Airflow, Azure ML) and model lifecycle automation.
• Deep understanding of MLOps, LLMOps, and AIOps frameworks for CI/CD, model retraining, evaluation, and deployment at scale.
• Proficient in observability and telemetry integration for AI systems — monitoring model health, drift, inference latency, and GPU utilization.
• Skilled in automated incident detection, correlation, and remediation using AI-driven observability and alerting systems.
• Knowledge of data governance, evaluation frameworks (Promptfoo, Portkey, RAG validation) and DevSecOps principles for AI pipelines.
• Design and operationalize the AI reliability stack, covering training, inference, and evaluation pipelines across Central Kitchen shared services.
• Implement automated health checks, anomaly detection, and retraining triggers for deployed LLM and RAG models.
• Collaborate with AI Safety and Security teams to enforce model guardrails, version control, and change governance in CI/CD workflows.
• Establish monitoring baselines and SLOs for AI workloads (throughput, latency, accuracy, drift) across all environments.
• Integrate AIOps-driven diagnostics to reduce mean time to detect (MTTD) and mean time to recover (MTTR) in AI platform operations.
• Support SRB/ARB reviews by maintaining clear operational documentation, evaluation evidence, and audit readiness of AI components

WHAT’S ON OFFER

You will be remunerated with an excellent base salary and entitled to attractive company benefits. Additionally, you will get the opportunity to enjoy a fun and collaborative work environment, alongside a strong career progression.

Your interest will be treated with strict confidentiality.