Artificial Intelligence Engineer

Marlabs LLC; Austin (TX); On-site; USD 120,000; Full time

Job summary

A competitive technology firm located in Austin, TX is looking for an AI Engineer to enhance their search platform by integrating semantic search and LLM techniques. The ideal candidate will have 10+ years of experience in AI/ML with a strong background in ElasticSearch, focusing on improving search relevance. Responsibilities include modernizing search systems, optimizing APIs, and deploying solutions on AWS. This position requires hands-on experience with the latest search technologies and cloud services.

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.
• Proficient in Python; familiarity with Java/Scala is a plus.

Responsibilities

• Modernize and enhance ElasticSearch system with semantic search techniques.
• Analyze limitations and enhance search relevance using synonyms and boosting strategies.
• Build and optimize search APIs for latency, relevance, and throughput.

Skills

ElasticSearch

Python

LLM

GenAI

Semantic Search

AWS

Re-Ranking

Search Engineering

Tools

Docker

FastAPI

OpenSearch

Job description

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.