ML Engineer - Search

Requirements:

• Strong experience in NLP, Recommender Systems, and Machine Learning algorithms (classification, regression, clustering, anomaly detection, pattern recognition techniques, deep learning, LLMs/RAG/Agentic AI).
• Hands-on experience building ML systems for Search & Personalisation use cases (AI-powered product search based on lexical search, semantic search, learning-to-rank algorithms, and recommendation systems).
• Deeper understanding of Transformer architecture, BERT and other embedding models for information retrieval techniques.
• Advancements in Large Language Models and Generative Modelling.
• Experience building deep learning models for next-purchase and next-basket recommendation (e. g., sequential/session-based recommenders based on next-purchase prediction) to personalise what and when customers are likely to buy next.

Key Technical Skills:

• Languages & Frameworks: Python; PyTorch/TensorFlow; NLTK, transformer-based embedding models (BERT and similar).
• Search & Retrieval: Lexical and semantic search, vector/ANN search, learning-to-rank algorithms and recommendation system design.
• ML Techniques: Classification, regression, clustering, anomaly detection, pattern recognition, deep learning, knowledge graphs, and LLMs/RAG/Agentic AI.
• Experience designing and building agentic workflows (multi-step reasoning, planning, tool-use, and orchestration) for product search and personalisation use cases.
• Hands-on experience with memory handling and context management at scale, including short-term/session memory and long-term user memory to support coherent, personalised agentic experiences across sessions.
• Experience integrating personalisation signals (user preferences, purchase history, intent) into agentic product search pipelines, enabling agents to retrieve, re-rank, and respond with results tailored to the individual user.
• Familiarity with techniques for managing large-scale context (context window optimisation, retrieval-augmented context injection, state/session management) across multi-turn, multi-agent interactions in production systems.

Preferred Skills:

• Experience designing and deploying ML solutions at large scale (billions of records).
• Experience leveraging parallel processing techniques (multithreading, multiprocessing, distributed computing) to build high-performance, scalable machine learning pipelines and optimise large-scale data processing workloads.
• Exposure to OpenSearch, ElasticSearch or Solr will be an added advantage.
• Familiarity with real-time data streaming technologies such as Kafka, Flink, etc.
• Understanding of the fundamentals of data governance, fairness, and ethical AI principles.

Required Qualifications:

• Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or equivalent practical experience.
• 3-6 years of relevant experience building large-scale ML systems.