Machine Learning Engineer - ML Systems

Role Summary\nThis role is suited for a candidate with deeper interest and capability in machine learning systems, model development, model evaluation, inference workflows, experimentation, and analytical problem-solving . The candidate will work on model-driven systems that support product matching, classification, similarity search, explainability, automated decisioning, and quality improvement.\nThe role requires someone who can understand ML model behavior, analyze outputs, improve inference pipelines, measure performance, and collaborate with science and engineering teams to translate model capabilities into production workflows.\nKey Responsibilities\nBuild, improve, and maintain ML inference pipelines and model-serving workflows.\nWork on model evaluation, error analysis, performance tracking, and experimentation.\nAnalyze model outputs to identify patterns, failure modes, precision / recall trade-offs, and improvement opportunities.\nCollaborate with Data Scientists to operationalize models into production-ready systems.\nSupport model training data preparation, labelling strategy, validation sample analysis, and benchmark creation.\nWork on ML systems involving embeddings, vector search, retrieval, classification, ranking, and match verdict generation.\nDevelop analytical scripts, notebooks, dashboards, and reports to measure model performance and business impact.\nHelp improve model explainability by surfacing signals, evidence, decision factors, and confidence indicators.\nSupport automation workflows where model predictions are used for decisioning and downstream product workflows.\nDebug production ML issues related to model outputs, drift, thresholds, retrieval quality, and data inconsistencies.\nContribute to building reusable ML utilities, evaluation frameworks, and experimentation workflows.\nRequired Skills\n2–5 years of relevant experience in ML Engineering, Data Engineering, Data Science Engineering, or Analytics Engineering.\nStrong programming skills. Good understanding of machine learning fundamentals, including classification, embeddings, similarity search, evaluation metrics, and model validation.\nExperience with ML libraries such as scikit-learn, PyTorch, TensorFlow, Hugging Face, sentence-transformers, or similar frameworks.\nAbility to perform model output analysis, error analysis, and metric-driven evaluation.\nStrong analytical skills with the ability to work with datasets, derive insights, and identify improvement opportunities.\nGood understanding of precision, recall, F1 score, confidence thresholds, false positives, false negatives, and sampling strategies.\nStrong in SQL , Python ML Libraries and data extraction for ML analysis.\nFamiliarity with model inference pipelines and production ML workflows.\nExperience with LLMs, prompt-based evaluation, RAG systems, or agentic AI workflows.\nAbility to write clean, modular, maintainable code.\nGood communication skills to explain model behavior, analysis findings, and technical trade-offs.\nGood to Have\nExperience with NLP, product matching, entity resolution, semantic search, or recommendation systems.\nExposure to vector databases or retrieval systems such as Milvus, FAISS, OpenSearch, Pinecone, S3 Vectors, or similar technologies.\nFamiliarity with ML Ops tools, model monitoring, experiment tracking, and deployment workflows.\nExperience working with e-commerce catalog data, product attributes, brand, size, pack, and taxonomy-related problems.\nAbility to build dashboards or analysis reports