Machine Learning Engineer (RnD)

we're looking for a Machine Learning Engineer (R&D) who thrives in the space between experimentation and execution. You don't need a PhD or a publication list — but you do need curiosity: the kind that makes you fine-tune a model just to see what happens, or prototype a RAG pipeline over a weekend. You've worked on real AI projects in industry, shipped code that touched users, and know that the best research is the kind that survives contact with production. If you're mid-level with strong potential, or experienced but still hungry to explore — and you care more about what works than what's novel — this is your kind of challenge. This is a hybrid role based in West Jakarta, where curiosity meets code. What You'll Do: ✅ Prototype and validate AI/ML concepts for real-world use cases: NLP, RAG, agents, or GenAI workflows ✅ Build lightweight pipelines for data prep, model training, evaluation, and serving ✅ Experiment with LLM tooling: prompt engineering, fine-tuning, retrieval, caching, fallback logic ✅ Collaborate with product and engineering to turn promising ideas into testable features ✅ Document experiments, measure impact, and iterate based on results — not just intuition ✅ Stay sharp on emerging techniques, but filter them through a lens of practicality Who You Are: ✅ 2–4 years in ML engineering, applied AI, or related roles — industry experience preferred, but strong mid-level candidates welcome ✅ Strong in Python and modern ML frameworks (PyTorch, Hugging Face, LangChain, LlamaIndex) ✅ Hands-on with LLM workflows: RAG, prompt design, evaluation, basic fine-tuning ✅ Comfortable with vector databases (Pinecone, Weaviate, pgvector) and API integration ✅ Bonus: Experience with MLOps basics (MLflow, Docker, FastAPI) or workflow tools (Dify, n8n) ✅ Fluent in English — written and spoken ✅ Nerdy enough to read a paper for fun — pragmatic enough to ask "does this actually help?"

Skills: Database Systems, Data Processing, ETL, Microsoft SQL Server, Python, MongoDB, Redis, Hadoop, Data Integration, Data Engineering