Job description for Machine Learning Operations Engineer at Cyber Olympus
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.
