Machine Learning Engineer Search, Retrieval & RAG

About the Role

We are looking for a Machine Learning Engineer to build next-generation Search, Retrieval, Ranking, and RAG systems that power intelligent AI experiences at scale.

This is not a “configure and optimize” role. You’ll build the RAG and relevance intelligence from the ground up—designing retrieval strategies, ranking models, evaluation frameworks, and AI experiences from first principles. It’s an opportunity to solve hard ML problems, experiment at scale, and leave your mark on the core platform.

You will work at the intersection of Machine Learning, Information Retrieval, NLP, and Generative AI, developing models and systems that understand user queries, retrieve the most relevant information, rank results effectively, and generate accurate, grounded responses.

This is a hands-on, end-to-end ML role spanning experimentation, model development, evaluation, and production deployment.

What You Will Do

- Build and optimize search retrieval, ranking, and Learning-to-Rank (LTR) models.
- Develop query understanding, intent classification, query rewriting, and query expansion solutions.
- Design semantic and hybrid retrieval using BM25, embeddings, and vector search.
- Build and optimize RAG pipelines covering document processing, chunking, embeddings, retrieval, reranking, and grounded generation.
- Fine-tune and evaluate embedding, reranking, and transformer-based models.
- Develop evaluation frameworks and golden datasets using metrics such as NDCG, MRR, Recall@K, Precision@K, faithfulness, and hallucination rate.
- Analyze search logs, user behavior, and relevance data to identify opportunities and continuously improve search quality.




- Work closely with Search and Software Engineers to optimize OpenSearch/Elasticsearch and production retrieval systems.
- Take ML solutions from prototype to production, optimizing for relevance, latency, scalability, and reliability.

Key Areas of Ownership Search Relevance | Retrieval | Ranking & Reranking | Query Understanding | Semantic Search | Embeddings | RAG | LLM Evaluation | Search Quality

What You Bring

- Solid foundation in Machine Learning, NLP, Information Retrieval, and Search Relevance.
- Strong Python programming skills with hands-on experience in PyTorch or TensorFlow.
- Experience with Hugging Face Transformers, Sentence Transformers, and Scikit-Learn.
- Practical experience with embeddings, vector search, BM25, ranking, reranking, and transformer models.
- Experience building, evaluating, and deploying production ML systems.
- Strong analytical skills and ability to translate complex search/retrieval problems into measurable improvements.

Good to Have

- Experience with RAG, LLM applications, prompt engineering, and LLM evaluation.
- Hands-on experience with OpenSearch/Elasticsearch.
- Experience with vector databases and large-scale retrieval systems.
- Experience working with search logs, clickstream data, relevance judgments, or recommendation systems.
- Knowledge of distributed systems and large-scale data processing.

Qualifications

- Bachelor's or Master's degree in Computer Science, AI, ML, Data Science, or a related field.
- Relevant experience in Machine Learning, NLP, Search, Information Retrieval, Recommendation, or Generative AI.

Impact You will help build high-quality, scalable search and AI retrieval experiences where relevance, accuracy, grounding, and speed directly impact the user experience.

You will have end-to-end ownership—from research and experimentation to production deployment and continuous optimization.

📌 Machine Learning RAG Engineer (Chandigarh)
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📍 Chandigarh