AI Engineer

6 days ago

Jakarta Metropolitan Area, Indonesia MSBU Konsultan Full time $80,000 - $120,000 per year

Min ; 3 Tahun Pengalaman

About the Role

We are seeking an AI Engineer to design and optimize recommendation engines and audience clustering models for streaming products (TV, Movies, Series, Microdrama). The role includes building prototypes (CLI, dashboards, or lightweight web apps) and collaborating with product, data, and engineering teams. Focus areas include ML engineering, data pipelines, and MLOps, with added value from experience in LLMs, embeddings, or vector search.

Key Responsibilities

  1. Recommendation & Clustering Models

● Build collaborative filtering, content-based, and hybrid recommendation models

● Develop user behavior clustering from watch history and engagement data

● Implement models in Python (scikit-learn, PyTorch, TensorFlow)

● Explore LLM-based recommendation and embedding retrieval

  1. Prototype Development

● Build CLI tools or internal dashboards (Streamlit, FastAPI, etc.)

● Create proof-of-concept systems for stakeholders

● Build simple A/B test simulators

  1. Data Engineering & ETL

● Handle streaming analytics, logs, sessions, and events

● Clean and transform data for modeling

● Write SQL pipelines (BigQuery, PostgreSQL)

  1. Platform/SaaS Integration

● Integrate with third-party recommendation or analytics APIs

● Build in-house components when needed

● Prepare models for batch or real-time deployment

  1. Model Evaluation & Monitoring

● Evaluate using , , NDCG, coverage, uplift

● Maintain model freshness and diversity

● Monitor drift and manage retraining cycles

Required Skills & Experience Core Technical Skills

● Strong Python skills (Pandas, NumPy, Scikit-Learn)

● Experience with clustering (KMeans, DBSCAN, HDBSCAN, GMM)

● Experience with recommendation systems (MF, CF, neural models)

● Strong SQL

● Understanding of ML validation and experimentation

● Ability to build small prototypes using FastAPI, Flask, Streamlit, Gradio, CLI tools

● PyTorch / TensorFlow

● Knowledge of embeddings and vector databases (FAISS, Pinecone, Weaviate, Chroma)

● Media/streaming analytics experience

● Familiarity with content-platform recommendation patterns

● Experience with GCP, AWS, or Azure

Example Projects

● Personalized Home Feed for streaming content

● "Recommended For You" ranking using embeddings

● Genre-based user segmentation

● Continue Watching and next-item recommendation

● Microdrama content grouping

● Watchtime prediction models

● Semantic search for content discovery

  • ● CTR prediction and thumbnail ranking

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