Lead AI Engineer (Search Modernization)

We are looking for an AI Engineer to modernize and enhance our existing regex/keyword-based ElasticSearch system by integrating state-of-the-art semantic search, dense retrieval, and LLM-powered ranking techniques.

This role will drive the transformation of traditional search into an intelligent, context-aware, personalized, and high-precision search experience. The ideal candidate has hands-on experience with ElasticSearch internals, information retrieval (IR), embedding-based search, BM25, re-ranking, LLM-based retrieval pipelines, and AWS cloud deployment.

Role

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.

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.

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.

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.

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.