AI Ops Engineer

ITCAN PTE. LIMITED; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

ITCAN PTE. LIMITED in Singapore seeks a Data & AI Operations Engineer to apply AI, ML, and LLM-based tools to improve operational reliability and efficiency. You will collaborate with senior engineers to turn prototypes into scalable production capabilities.

The role focuses on observability, incident detection, and automation, with end-to-end responsibility for AIOps, MLOps, and LLMOps solutions in telecom-like environments. You should be hands-on, proactive, and eager to learn.

Qualifications

• Bachelor’s or Master’s degree in Computer Science or a related field.
• 1–2 year experience in Python, SQL, Apache Spark, or similar languages.
• Data Wrangling, Analysis and Visualisation.
• Containers and deployment fundamentals (Docker; Kubernetes).
• Version Control (Git).
• Testing (pytest).
• MLOps/GenAI Ops tools.
• Internships, graduate projects, hackathons, open-source contributions, or relevant work experience in Software development, Automation, AI/ML-related projects.
• Familiarity with AI / ML tooling and telco environment.

Responsibilities

• Apply AI, ML, and LLM-based tools to solve real-world operational and reliability challenges.
• Identify, prototype, and validate AI-driven improvements to day-to-day operations using clear, measurable metrics.
• Operationalise successful AI use cases into reliable, scalable, and maintainable production capabilities.
• Improve system observability, incident detection, root-cause analysis, and operational efficiency through automation and intelligence.
• Support the end-to-end lifecycle of AIOps, MLOps, and LLMOps solutions, from experimentation to production.
• Ensure operational reliability, robustness, and maintainability of AI-enabled systems.
• Collaborate closely with senior engineers and operations teams to align solutions with business and operational needs.
• Continuously evaluate emerging AI technologies and tools for practical applicability and impact.

Skills

Python

SQL

Apache Spark

Data Wrangling

Visualization

Container tech

Docker

Kubernetes

Git

Testing

MLOps/GenAI Ops

Logs/Monitoring

Cloud ML services

AI/GenAI tooling

Education

Bachelor’s or Master’s degree in Computer Science or related field

Tools

Docker

Kubernetes

Git

Databricks

Azure ML

MLFlow

Prometheus

Grafana

OpenTelemetry

Job description

Data & AI Operations Engineer #AIDA

Be a Part of Something BIG!

• Apply AI, ML, and LLM-based tools to solve real-world operational and reliability challenges
• Identify, prototype, and validate AI-driven improvements to day-to-day operations using clear, measurable metrics
• Operationalise successful AI use cases into reliable, scalable, and maintainable production capabilities
• Improve system observability, incident detection, root-cause analysis, and operational efficiency through automation and intelligence
• Support the end-to-end lifecycle of AIOps, MLOps, and LLMOps solutions, from experimentation to production
• Ensure operational reliability, robustness, and maintainability of AI-enabled systems
• Collaborate closely with senior engineers and operations teams to align solutions with business and operational needs
• Continuously evaluate emerging AI technologies and tools for practical applicability and impact
• Embed best practices for monitoring, governance, and responsible use of AI in operational environments

Make an Impact by:

• Explore and evaluate AI, ML, and LLM-based tools that can improve operational efficiency and visibility
• Build small proof-of-concepts or prototypes to validate ideas in operational contexts
• Measure outcomes using simple, clearly defined metrics
• Analyze logs, metrics, traces, and events to identify anomalies and automation opportunities
• Assist in deploying, monitoring, and maintaining ML or LLM-enabled solutions
• Automate repetitive operational tasks using scripts, workflows, or lightweight services
• Work with senior engineers to turn validated ideas into stable, scalable capabilities
• Document solutions, trade-offs, and lessons learned for reuse by other teams
• Stay informed on relevant AI and open-source tooling, and suggest ideas worth testing

Skills for Success:

• Bachelor’s or Master’s degree in Computer Science or a related field
• 1–2 year experience:
• Python, SQL, Apache Spark, or similar languages
• Data Wrangling, Analysis and Visualisation
• Containers and deployment fundamentals (Docker; Kubernetes)
• Version Control (Git)
• Testing (pytest)
• MLOps/GenAI Ops tools
• Internships, graduate projects, hackathons, open-source contributions, or relevant work experience in:
• Software development
• Automation
• AI / ML / GenAI-related project
• Familiarity with
• Software engineering best practices, including OO/Functional design, reproducibility, and testing
• Logs, metrics, and monitoring concepts
• ML/GenAI tooling (LLM, RAG, LlamaGuard, Presidio, MLFlow, LangFuse etc)
• Monitoring and observability stacks (Prometheus, Grafana, OpenTelmetry)
• Hands-on experience with cloud ML services (Databricks, Azure ML, etc.)
• Hands-on, curious, and proactive
• Pragmatic and focused on operational impact
• Disciplined about scaling what works and discarding what doesn’t
• Collaborative and able to communicate ideas clearly
• Self-driven and proactive, comfortable working in a fast-paced environment
• Familiarity with AI and data development process in telco environment