Data Scientists (Search Modernization) For Move.com

AI Engineer

ElasticSearch internals

Modernizing the Search Platform

• Analyze limitations in current

  regex & keyword-only

  search implementation on ElasticSearch.
• Enhance search relevance using:
  • BM25 tuning
  • Synonyms, analyzers, custom tokenizers
  • Boosting strategies and scoring optimization
• Introduce

  semantic / vector-based search

  using dense embeddings.

2. LLM-Driven Search & RAG Integration

• Implement

  LLM-powered search

  workflows including:
  • Query rewriting and expansion
  • Embedding generation (OpenAI, Cohere, Sentence Transformers, etc.)
  • Hybrid retrieval (BM25 + vector search)
  • Re-ranking using cross-encoders or LLM evaluators
• Build

  RAG (Retrieval Augmented Generation)

  flows using ElasticSearch vectors, OpenSearch, or AWS-native tools.

3. Search Infrastructure Engineering

• Build and optimize search APIs for latency, relevance, and throughput.
• Design scalable pipelines for:
  • Indexing structured and unstructured text
  • Maintaining embedding stores
  • Real-time incremental updates
• Implement caching, failover, and search monitoring dashboards.

4. AWS Cloud Delivery

• Deploy and operate solutions on

  AWS

  , leveraging:
  • OpenSearch Service or EC2-managed ElasticSearch
  • Lambda, ECS/EKS, API Gateway, SQS/SNS
  • SageMaker for embedding generation or re-ranking models
• Implement CI/CD for search models and pipelines.

5. Evaluation & Continuous Improvement

• Develop search evaluation metrics (nDCG, MRR, precision@k, recall).
• Conduct A/B experiments to measure improvements.
• Tune ranking functions and hybrid search scoring.
• Partner with product teams to refine search behaviors with real usage patterns.

Required Skills & Qualifications

• 5–10 years of experience in

  AI/ML, NLP, or IR systems

  , with hands-on search engineering.
• Strong expertise in

  ElasticSearch/OpenSearch

  : analyzers, mappings, scoring, BM25, aggregations, vectors.
• Experience with

  semantic search

  :
  • Embeddings (BERT, SBERT, Llama, GPT-based, Cohere)
  • Vector databases or ES vector fields
  • Approximate nearest neighbor (ANN) techniques
• Working knowledge of

  LLM-based retrieval

  and

  RAG architectures

  .
• Proficient in

  Python

  ; familiarity with Java/Scala is a plus.
• Hands-on AWS experience (OpenSearch, SageMaker, Lambda, ECS/EKS, EC2, S3, IAM).
• Experience building and deploying APIs using

  FastAPI/Flask

  and containerizing with

  Docker

  .
• Familiar with typical IR metrics and search evaluation frameworks.

Preferred Skills

• Knowledge of

  cross-encoder and bi-encoder

  architectures for re-ranking.
• Experience with

  query understanding

  , spell correction, autocorrect, and autocomplete features.
• Exposure to

  LLMOps / MLOps

  in search use cases.
• Understanding of

  multi-modal search

  (text + images) is a plus.
• Experience with

  knowledge graphs

  or metadata-aware search.