Machine Learning Engineer - Recommendation Systems

Job Title Machine Learning Engineer – Recommendation Systems Location Bangalore Experience 3–8 years (flexible based on depth in ML systems) Job Description We are looking for a Machine Learning Engineer (Recommendations) to design, build, and scale personalized recommendation systems that power discovery, ranking, and user engagement across our products.
You will work at the intersection of machine learning, data engineering, and backend systems, taking models from research to production.
Key ResponsibilitiesRecommendation & ML Design and develop recommendation systems including: Collaborative Filtering (user-item, item-item) Content-based and hybrid recommenders Ranking and re-ranking models Embedding-based retrieval (ANN, vector search) Train, evaluate, and iterate on models using offline metrics (NDCG, MAP, Recall@K) and online A/B experiments Build pipelines for feature engineering, model training, inference, and retraining Production ML & Systems Deploy ML models in production environments with low-latency constraints Optimize inference for scale (caching, batching, approximate nearest neighbors) Build real-time and batch recommendation pipelines Monitor model performance, data drift, and system health Data & Experimentation Work with large-scale datasets (clicks, impressions, transactions) Define success metrics for recommendations (CTR, CVR, retention) Run and analyze A/B tests and iterate based on results.
Collaboration Work closely with product, data, and backend teams to translate business problems into ML solutions Contribute to ML best practices, documentation, and system design Required SkillsCore ML Strong understanding of: Recommendation algorithms Ranking and learning-to-rank Embeddings and similarity search Experience with Python and ML libraries (PyTorch / TensorFlow / Scikit-learn) Data & Systems Strong SQL skills; experience with large datasets Experience with feature stores, data pipelines, and batch/stream processing Familiarity with vector databases / ANN libraries (FAISS, ScaNN, Elasticsearch/OpenSearch KNN, Milvus) Production & Infra Experience deploying models using REST/gRPC services Familiarity with Docker, Kubernetes, or cloud platforms (AWS / GCP / Azure) Understanding of latency, throughput, and scalability trade-offs Good to Have Experience with: Search or feed ranking systems Hybrid retrieval (BM25 + embeddings) Real-time recommendations Knowledge of: Kafka / streaming systems MLOps tools (MLflow, Airflow) Experience in e-commerce, ads, content platforms or marketplaces What You’ll Work On Personalized home feeds and search ranking “People also viewed” recommendations Cold-start and long-tail problems Large-scale experimentation and model optimization Nice Behavioral Traits Strong problem-solving and system-thinking mindset Ability to balance model quality vs production constraints Comfortable owning models end-to-end