Marlabs LLC is hiring: Artificial Intelligence Engineer in Austin
Marlabs LLC, Austin, TX, United States
Base pay range
$120,000.00/yr - $120,000.00/yr
Location
Location: Austin, TX (3 days' work from office)
Discovery Phase
During the discovery stage, it will be 5 days working from office for the first 4 weeks of discovery
Profiles
The profiles we are getting mostly are on their recent GenAI experience only. We need Strong Data Scientist and ML Engineer/GenAI Engineer background, fine if elastic search is not available,
Mandatory Skills
ElasticSearch, 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 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 .
Modernizing the Search Platform
Analyze limitations in current regex & keyword-only search implementation on ElasticSearch.
Enhance search relevance using:
Synonyms, analyzers, custom tokenizers
Boosting strategies and scoring optimization
Introduce semantic / vector‑based search using dense embeddings.
Implement LLM‑powered search workflows including:
Query rewriting and expansion
Embedding generation (OpenAI, Cohere, Sentence Transformers, etc.)
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.
Deploy and operate solutions on AWS , leveraging:
OpenSearch Service or EC2‑managed ElasticSearch
Lambda, ECS/EKS, API Gateway, SQS/SNS
Implement CI/CD for search models and pipelines.
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
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
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.
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$120,000.00/yr - $120,000.00/yr
Location
Location: Austin, TX (3 days' work from office)
Discovery Phase
During the discovery stage, it will be 5 days working from office for the first 4 weeks of discovery
Profiles
The profiles we are getting mostly are on their recent GenAI experience only. We need Strong Data Scientist and ML Engineer/GenAI Engineer background, fine if elastic search is not available,
Mandatory Skills
ElasticSearch, 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 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 .
Modernizing the Search Platform
Analyze limitations in current regex & keyword-only search implementation on ElasticSearch.
Enhance search relevance using:
Synonyms, analyzers, custom tokenizers
Boosting strategies and scoring optimization
Introduce semantic / vector‑based search using dense embeddings.
Implement LLM‑powered search workflows including:
Query rewriting and expansion
Embedding generation (OpenAI, Cohere, Sentence Transformers, etc.)
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.
Deploy and operate solutions on AWS , leveraging:
OpenSearch Service or EC2‑managed ElasticSearch
Lambda, ECS/EKS, API Gateway, SQS/SNS
Implement CI/CD for search models and pipelines.
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
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
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.
#J-18808-Ljbffr