Machine Learning Operations Engineer

Mandatory Being able to speak english fluently at professional setting Being able to relocate to Malaysia Having relevant experiences / role at least 3 years

MLOps Skill Set Software Engineering: Python, REST/gRPC APIs, testing, modular code, Git. Infrastructure & DevOps: Docker, Kubernetes, CI/CD, Infrastructure-as-Code (Terraform). Data & ML Foundations: ML lifecycles, model drift monitoring, data engineering, feature stores. Production Operations: Model deployment, low-latency API serving, system autoscaling.

MLOps Tech Stack Containers & Clouds: Docker, Podman, Kubernetes, AWS/Google Could Platform/Azure. Data & Feature Engineering: Spark, Data Version Control, Feast. Orchestration: Airflow, Kubeflow, Prefect. Tracking & Governance: MLflow, Weights & Biases. Model Serving: FastAPI, Ray Serve, Triton Inference Server. Monitoring & CI/CD: GitHub Actions, Prometheus, Grafana, Evidently Al.

Data Lakehouse Skill Set Table Formats & Storage: Apache Iceberg/Delta Lake mechanics, schema evolution, file compaction. Distributed Processing: Query optimization, memory tuning, parallel compute architectures. Data Architecture: Medallion design (Bronze/Silver/Gold), Change Data Capture (CDC), dimensional modelling. Governance & FinOps: Unified security, data lineage, compute-storage cost management.

Data Lakehouse Tech Stack Storage Layer: AWS S3, Google Cloud Storage, Azure Data Lake. Table Formats: Apache Iceberg, Delta Lake, Apache Hudi. Compute & Query Engines: Apache Spark, Databricks, Trino, StarRocks, Flink. Catalog & Governance: Unity Catalog, Apache Polaris, AWS Glue, Atlan. Transformation & Workflow: Data build tool (dbt), Apache Airflow, Dagster. Quality & Observability: Great Expectations, Monte Carlo.

Skills: API Development, Apache Spark, Kubernetes, AWS Lambda, Artificial Intelligence, Amazon Web Services (AWS), Docker, Redis, Machine Learning, GIT, Google Cloud Platform, Azure