Senior ML Engineer - Build Scalable AI Systems & Pipelines

insignia.co.id; Indonesia; Hybrid; IDR 223,200,000 - 390,600,000; Full time

Job summary

Insignia is seeking a Machine Learning Engineer to bridge data science and production engineering. You will design, train, and deploy production-grade ML systems powering real-world intelligence from data ingestion to API serving.

The role is hybrid in West Jakarta, with a focus on scalable pipelines and efficient inference. You will collaborate with data engineers and backend teams, optimize models for latency and cost, and implement robust MLOps practices to ensure reliability and

Qualifications

• 2–5 years of experience in machine learning engineering or applied AI.
• Proficient in Python, PyTorch/TensorFlow, and modern ML frameworks.
• Experience deploying models with FastAPI, Flask, Docker, or cloud endpoints.
• Familiar with MLOps tools (MLflow, Kubeflow, SageMaker, or similar).
• Solid software engineering practices (testing, CI/CD, system design).
• Fluent in English — written and spoken.
• Pragmatic, systems-oriented, and value-driven about real-world AI.

Responsibilities

• Design, train, and deploy production-grade ML systems from data ingestion to API serving.
• Build and maintain ML pipelines for training, validation, and continuous serving.
• Optimize models for performance, cost, and scalability (quantization, batching, caching).
• Implement monitoring for data drift, model decay, and system health.
• Collaborate with data engineers and backend teams to integrate ML into products.
• Establish best practices for MLOps, versioning, and reproducibility.

Skills

Python

PyTorch

TensorFlow

FastAPI

MLflow

Kubeflow

CI/CD

Cloud platforms

Tools

Docker

Kubernetes

Job description

Insignia is seeking a Machine Learning Engineer to bridge data science and production engineering. You will design, train, and deploy production-grade ML systems powering real-world intelligence from data ingestion to API serving.

The role is hybrid in West Jakarta, with a focus on scalable pipelines and efficient inference. You will collaborate with data engineers and backend teams, optimize models for latency and cost, and implement robust MLOps practices to ensure reliability and