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Vector Databases Jobs in Georgetown, TX (NOW HIRING)

STI

Lead AI Engineer

Austin, TX · On-site

$101K - $133K/yr

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 ...

Outdoorsy

AI Data Engineer

Austin, TX · On-site

$113K - $136K/yr

Through Knowledge Graphs, Vector Databases, and Retrieval-Automated Generation, ensure agents can access e.g. latest policy status, upsell propensity scores, and customer history in near real-time.

Outdoorsy

AI Data Engineer

Austin, TX

$113K - $136K/yr

Through Knowledge Graphs, Vector Databases, and Retrieval-Automated Generation, ensure agents can access e.g. latest policy status, upsell propensity scores, and customer history in near real-time.

LPL Financial

AVP, AI Solutions Engineer

Austin, TX · Hybrid

$146K - $244K/yr

Build knowledge bases and embedding models for contextual reasoning using vector databases (Pinecone, OpenSearch). * Apply memory management techniques for multi-agent orchestration (short-term and ...

Eitacies Inc

GenAI Architect Finance

Austin, TX

$60/hr

Vector databases (Pinecone, FAISS, etc.) * NLP, document processing, or conversational AI systems * Experience working in large-scale enterprise environments Required Skills: * Artificial ...

Outdoorsy

AI Data Engineer

Austin, TX · On-site

$113K - $136K/yr

Through Knowledge Graphs, Vector Databases, and Retrieval-Automated Generation, ensure agents can access e.g. latest policy status, upsell propensity scores, and customer history in near real-time.

eStaffLLC

Senior Software Developer Specialist

Austin, TX

$54 - $71.25/hr

Databases: Proficiency in SQL (PostgreSQL, MySQL) and NoSQL/vector databases. * Scripting: Proficient in both Bash and PowerShell for automation workflows. Preferred Qualifications * Experience with ...

New

eStaffLLC

Senior Software Developer Specialist

Austin, TX

$54 - $71.25/hr

Databases: Proficiency in SQL (PostgreSQL, MySQL) and NoSQL/vector databases. Scripting: Proficient in both Bash and PowerShell for automation workflows. Preferred Qualifications * Experience with ...

New

webAI Inc

Senior Machine Learning Engineer

Austin, TX · On-site

$103K - $142K/yr

Build and maintain Retrieval Augmented Generation (RAG) pipelines, including vector database integration for contextual retrieval. * Work with multi-modal AI systems across computer vision, audio ...

Continued

AI Engineer (Remote)

Austin, TX · Remote

Create data pipelines and AI model context protocols, leveraging tools like MCP, LangChain, vector databases, and semantic search. * Reliability & Security: Ensure the high availability, performance ...

continued

AI Engineer (Remote)

Austin, TX · On-site +1

Create data pipelines and AI model context protocols, leveraging tools like MCP, LangChain, vector databases, and semantic search. * Reliability & Security: Ensure the high availability, performance ...

Continued

AI Engineer (Remote)

Austin, TX · Remote

Create data pipelines and AI model context protocols, leveraging tools like MCP, LangChain, vector databases, and semantic search. * Reliability & Security: Ensure the high availability, performance ...

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All JobsVector Databases JobsVector Databases Jobs in Georgetown, TX

Vector Databases information

What are vector databases?

Vector databases are specialized databases designed to store, manage, and search high-dimensional vector data, which is commonly generated from machine learning models, such as embeddings from natural language processing or image recognition. They enable efficient similarity search operations, such as finding the most similar items to a given query vector, which is essential for applications like recommendation systems, semantic search, and AI-powered search engines. Unlike traditional databases that handle structured or unstructured data, vector databases are optimized for fast and scalable similarity searches on large datasets of vectors.

What are some common challenges faced when working with vector databases, and how can they be addressed?

Professionals working with vector databases often encounter challenges such as efficiently scaling to handle large datasets, ensuring low-latency similarity searches, and integrating the database with machine learning pipelines. To address these, teams typically implement distributed architectures, fine-tune indexing strategies, and collaborate closely with data engineers and machine learning specialists. Staying updated with the latest developments in vector database technologies and maintaining clear communication with cross-functional teams are also key to overcoming these challenges.

What is the difference between Vector Databases vs Data Engineers?

AspectVector DatabasesData Engineers
Required SkillsDatabase management, data modeling, query optimizationData pipeline development, ETL processes, programming
Work EnvironmentData storage systems, AI/ML projects, cloud platformsData infrastructure, cloud environments, big data tools
Industry UsageAI, machine learning, recommendation systemsData integration, analytics, data architecture

While Vector Databases focus on storing and querying high-dimensional vector data for AI applications, Data Engineers build and maintain data pipelines and infrastructure to support data analysis and machine learning workflows. Both roles are essential in data-driven industries but serve different functions within the data ecosystem.

What are the key skills and qualifications needed to thrive as a Vector Database Engineer, and why are they important?

Success as a Vector Database Engineer requires a strong background in computer science, database management, and experience with machine learning or AI-driven data systems. Familiarity with vector database platforms (such as Pinecone, Milvus, or Weaviate), cloud infrastructure, and proficiency in languages like Python are typically expected. Strong problem-solving skills, effective communication, and the ability to work cross-functionally help engineers stand out. These competencies are vital to efficiently design, deploy, and maintain scalable vector search solutions that power modern AI applications.
What are popular job titles related to Vector Databases jobs in Georgetown, TX? For Vector Databases jobs in Georgetown, TX, the most frequently searched job titles are:
What job categories do people searching Vector Databases jobs in Georgetown, TX look for? The top searched job categories for Vector Databases jobs in Georgetown, TX are:
What cities near Georgetown, TX are hiring for Vector Databases jobs? Cities near Georgetown, TX with the most Vector Databases job openings:
Vector Databases jobs near you

Lead AI Engineer

STI

Austin, TX • On-site

$101K - $133K/yr

Full-time

Posted 24 days ago

Job description

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.

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