Work Location: 98 Hoang Quoc Viet, Cau Giay, Hanoi
Working Hours: 8:30 AM – 5:30 PM (Monday to Friday)
Salary Range: VND 30–70 million

What You’ll Do:

Design and operate scalable, secure ML platform infrastructure supporting offline training, batch scoring, and real-time inference.
Build and maintain core ML platform services such as model registry, versioning, champion/challenger workflows, feature ingestion and serving, and deployment orchestration.
Standardize ML workflows from experiment → approval → production.
Build and maintain CI/CD pipelines for ML and platform components, including automated deployment, environment promotion (UAT → Production), rollback, and recovery.
Deploy and operate model serving systems via REST / gRPC APIs and batch scoring pipelines.
Support advanced deployment strategies such as shadow, canary, and A/B testing.
Ensure low latency, high availability, and strong observability for scoring services.
Collaborate with Data Engineering teams to build and operate offline and online Feature Stores, ensuring feature consistency between training and serving.
Design and implement monitoring for model performance, data quality, feature stability, and system health.
Build early-warning mechanisms for model decay, data drift, and production anomalies.
Support model governance, auditability, and compliance in credit-risk contexts.
Work closely with Data Scientists, Risk, Fraud, Backend, and Infrastructure teams.
Enable Data Science teams to move from notebook to production safely.

What We’re Looking For:

Proven experience as an ML Platform Engineer, MLOps Engineer, or Platform / Software Engineer working with ML systems.
Strong Python skills for platform services, automation, and ML tooling.
Solid understanding of the ML lifecycle in production environments.
Experience with containerization and orchestration (Docker, Kubernetes).
Experience building and operating production APIs (FastAPI, gRPC).
Strong understanding of batch and real-time scoring systems.
Familiarity with CI/CD pipelines and infrastructure-as-code mindset.
Strong system-thinking, with ability to design scalable, observable, and governed platforms.
Excellent communication skills and strong ownership mindset.

Tech Stack (at a glance)

Data & Storage: MinIO, Iceberg, ClickHouse
Processing: Spark, Polars, Airflow, Pathway
ML & MLOps: H2O.ai, MLflow
Serving: FastAPI / gRPC
Platform: Docker, Kubernetes, CI/CD (Git-based)
Monitoring & Governance: Prometheus, Grafana, model & data observability

Nice to Have (Bonus)

Experience in credit scoring, risk, fraud, or financial services.
Experience with Feature Stores and Lakehouse-style data platforms.
Experience with streaming or real-time data systems.
Knowledge of model governance, approval workflows, and A/B testing for decision systems.

Why Join VC Scoring Team

Build the core ML platform powering real financial decisions.
Work on production-grade ML systems with high impact and responsibility.
Own architectural decisions and influence long-term platform strategy.
Collaborate with strong Data, Risk, and Engineering teams.