Job Title
:
Lead AI Engineer
Location:
Austin, Texas (Hybrid)
Duration: Longterm Contract
Lead AI Engineer (Search Modernization)
Mandatory Skills: Elastic Search, OpenSearch, Python, LLM, GenAI, Semantic Search, Re-Ranking, AWS, Search Engineer
Job Description:
We are looking for an
AI Engineer
to modernize and enhance our existing
regex/keyword-based Elastic Search 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
Elastic Search 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.