Machine Learning Engineer

Job opportunity for Machine Learning Engineer at PT Karsa Omni Teknologi Adicipta in KOTA ADM. JAKARTA BARAT, DKI JAKARTA, Indonesia.

Qualification:

we’re looking for a Machine Learning Engineer who bridges the gap between data science and production engineering. You’ve trained models, yes — but more importantly, you’ve deployed them, monitored them, and fixed them when they drifted.

You’ll work on applied AI projects across NLP, computer vision, recommendation systems, or GenAI — building scalable pipelines, optimizing inference, and ensuring reliability at scale. If you care more about latency, accuracy in production, and maintainability than leaderboard scores, this is your kind of challenge.

This is a hybrid role based in West Jakarta, where your code powers intelligent features that real users interact with every day.

What You’ll Do:

✅ Design, train, and deploy production-grade ML systems — from data to API

✅ Build and maintain ML pipelines for training, validation, and serving

✅ Optimize models for performance, cost, and scalability (batch or real-time)

✅ Collaborate with data engineers, software teams, and product leads to integrate ML into applications

✅ Implement monitoring for data drift, model decay, and system health

✅ Write clean, testable code in Python using modern MLOps practices

Who You Are:

✅ 2–5 years of experience in machine learning engineering or applied AI

✅ Strong in Python, PyTorch/TensorFlow, and ML frameworks (scikit-learn, Hugging Face, etc.)

✅ Experience with model deployment (FastAPI, Flask, Docker, cloud endpoints)

✅ Familiar with MLOps tools: MLflow, Kubeflow, SageMaker, or custom pipelines

✅ Comfortable working in cloud environments (AWS/GCP/Azure) and containerized systems

✅ Bonus: Hands-on with RAG, LLM fine-tuning, vector databases, or real-time inference

✅ Fluent in English — written and spoken

✅ Practical mindset — you prioritize impact, reliability, and iteration over novelty

Why Join Us?

Because great AI isn’t measured in papers — it’s measured in uptime, user trust, and real-world utility. If you're ready to build ML systems that endure, let’s talk.