ML Ops Integration Engineer

Singapore | Alexandra Road
12-Month Renewable Contract
Banking & Financial Services Technology
Be at the forefront of Enterprise AI Transformation

Build the Future of AI in Banking

Are you passionate about deploying cutting-edge AI solutions at scale?

We're looking for an ML Ops Integration Engineer to join a leading banking technology team driving the next generation of Generative AI, Agentic AI, and Enterprise AI Platforms. This is an exciting opportunity to work alongside AI engineers, architects, and cloud specialists to transform innovative AI concepts into secure, scalable, production-ready solutions.

If you're excited by AI infrastructure, cloud technologies, container platforms, automation, and large-scale enterprise integration, we'd love to hear from you.

What You'll Be Doing

As an ML Ops Integration Engineer, you will play a key role in bridging AI innovation with enterprise production environments.

Key Responsibilities

✔ Build, deploy, and integrate Generative AI and Agentic AI solutions into enterprise platforms

✔ Design and automate AI deployment pipelines using Python, Shell Scripting, and modern DevOps practices

✔ Deploy AI applications and services on container platforms such as OpenShift, Docker, and Kubernetes

✔ Integrate AI systems across cloud and on-premise environments

✔ Support CI/CD pipelines and infrastructure automation for AI workloads

✔ Collaborate with AI Engineers, Data Scientists, Architects, Security Teams, and DevOps Engineers

✔ Implement observability, monitoring, logging, and performance optimization frameworks

✔ Ensure security, compliance, scalability, and reliability of AI systems

✔ Drive continuous improvement and operational excellence across AI platforms

What We're Looking For

Required Skills & Experience

3–5 years of experience in MLOps, AI Engineering, Platform Engineering, DevOps, or System Integration

Strong hands-on experience with Linux / Unix environments and Bash scripting

Proficiency in Python for automation, integration, and AI-related implementations

Experience with Docker, Kubernetes, and containerized deployments

Exposure to OpenShift environments

Strong knowledge of cloud platforms, particularly AWS

Experience with API integration and microservices architecture

Understanding of CI/CD pipelines and infrastructure automation

Experience with logging, monitoring, and observability tools

Strong troubleshooting and problem-solving skills

Highly Preferred

Experience deploying Generative AI solutions

Hands-on exposure to RAG (Retrieval-Augmented Generation) architectures

Understanding of Agentic AI concepts and workflows

Experience with observability platforms such as Grafana, OpenTelemetry, Elastic Stack, Langfuse, Splunk, or Geneos

Knowledge of Workflow Orchestration tools such as Ctrl-M

Exposure to AI governance, security, and compliance frameworks

Experience working in large enterprise or banking environments