Lead AI Engineer (Search Modernization)

Lead AI Engineer (Search Modernization)
Location: Austin, TX (3 days work from office)
Client: Move Realtor/Tavant

Mandatory Skills: ElasticSearch, OpenSearch, Python, LLM, GenAI, SemanticSearch, Re-Ranking, AWS, Search Engineer

Job Description:

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.

Roles & Responsibilities

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.