... vector databases to enhance LLM outputs. • Strong understanding of prompt engineering, fine-tuning, and evaluation of generative models for real-world applications. Ability to build, optimize, and ...
... vector databases to enhance LLM outputs. • Strong understanding of prompt engineering, fine-tuning, and evaluation of generative models for real-world applications. Ability to build, optimize, and ...
Qualifications Required: * 2+ years of analytics consulting or industry experience * 2+ years of experience with artificial intelligence development tools, including vector databases such as Pinecone ...
Qualifications Required: * 2+ years of analytics consulting or industry experience * 2+ years of experience with artificial intelligence development tools, including vector databases such as Pinecone ...
CTIO AI Engineering Manager
Jacksonville, FL · On-site
$73K - $244K/yr
... vector databases and orchestration tools like LangChain - Translating complex business problems into software-engineered AI solutions - Deploying on cloud platforms like AWS, GCP, Azure ...
CTIO AI Engineering Manager
Jacksonville, FL · On-site
$73K - $244K/yr
... vector databases and orchestration tools like LangChain - Translating complex business problems into software-engineered AI solutions - Deploying on cloud platforms like AWS, GCP, Azure ...
Senior Cloud Engineer AI
$81K - $151K/yr
... vector databases and embedding models Knowledge of model optimization and inference acceleration Background in financial services or banking Salary : $81,400.00 - $151,800.00 Pay Type: Salaried The ...
Senior Cloud Engineer AI
$81K - $151K/yr
... vector databases and embedding models Knowledge of model optimization and inference acceleration Background in financial services or banking Salary : $81,400.00 - $151,800.00 Pay Type: Salaried The ...
... vector RAG over a capability registry) plus closed-set LLM selection with JSON-schema-constrained ... between agents and databases; LLMs never touch DBs directly. • Partner-team onboarding ...
... vector RAG over a capability registry) plus closed-set LLM selection with JSON-schema-constrained ... between agents and databases; LLMs never touch DBs directly. • Partner-team onboarding ...
Manage and optimize data retrieval using Elasticsearch, NoSQL databases, and Graph databases like ... of vector embeddings, semantic search, and RAG architectures. • Experience with Cloud ...
Manage and optimize data retrieval using Elasticsearch, NoSQL databases, and Graph databases like ... of vector embeddings, semantic search, and RAG architectures. • Experience with Cloud ...
... vector storage and retrieval. • Extensive experience with relational databases and general knowledge of NoSQL databases • Exposure to microservice architecture and cloud-native services ...
... vector storage and retrieval. • Extensive experience with relational databases and general knowledge of NoSQL databases • Exposure to microservice architecture and cloud-native services ...
Google AI Architect
Jacksonville, FL · On-site
Build RAG and agentic solutions using Vertex AI Vector Search and BigQuery vector; implement ... databases. Should have experience in leveraging various GenAI tools to accelerate software ...
Google AI Architect
Jacksonville, FL · On-site
Build RAG and agentic solutions using Vertex AI Vector Search and BigQuery vector; implement ... databases. Should have experience in leveraging various GenAI tools to accelerate software ...
... query databases, and interact with enterprise tools to perform real-world actions beyond text ... embeddings, vector search, RAG pipelines, model limitations, and how to ground LLM responses in ...
... query databases, and interact with enterprise tools to perform real-world actions beyond text ... embeddings, vector search, RAG pipelines, model limitations, and how to ground LLM responses in ...
... databases, and cloud-native services. • Develop modular and reusable components for LLM inference, vector search, embeddings, and model orchestration. • Integrate LLMs with enterprise systems ...
... databases, and cloud-native services. • Develop modular and reusable components for LLM inference, vector search, embeddings, and model orchestration. • Integrate LLMs with enterprise systems ...
Familiarity with vector databases and retrieval infrastructure such as Pinecone, Weaviate, or Milvus. Exposure to model adaptation and fine-tuning techniques such as LoRA or QLoRA. Understanding of ...
Familiarity with vector databases and retrieval infrastructure such as Pinecone, Weaviate, or Milvus. Exposure to model adaptation and fine-tuning techniques such as LoRA or QLoRA. Understanding of ...
Manage The Vector Learning Manager (VLM) - work with SME and other training resources to ensure all ... Demonstrated knowledge of Microsoft Suite applications database entry. * Demonstrated ability to ...
Manage The Vector Learning Manager (VLM) - work with SME and other training resources to ensure all ... Demonstrated knowledge of Microsoft Suite applications database entry. * Demonstrated ability to ...
Training Specialist
Starke, FL · On-site
Manage The Vector Learning Manager (VLM) - work with SME and other training resources to ensure all ... Demonstrated knowledge of Microsoft Suite applications database entry. * Demonstrated ability to ...
Training Specialist
Starke, FL · On-site
Manage The Vector Learning Manager (VLM) - work with SME and other training resources to ensure all ... Demonstrated knowledge of Microsoft Suite applications database entry. * Demonstrated ability to ...
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?
| Aspect | Vector Databases | Data Engineers |
|---|---|---|
| Required Skills | Database management, data modeling, query optimization | Data pipeline development, ETL processes, programming |
| Work Environment | Data storage systems, AI/ML projects, cloud platforms | Data infrastructure, cloud environments, big data tools |
| Industry Usage | AI, machine learning, recommendation systems | Data 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 Jacksonville, FL? For Vector Databases jobs in Jacksonville, FL, the most frequently searched job titles are:
What job categories do people searching Vector Databases jobs in Jacksonville, FL look for? The top searched job categories for Vector Databases jobs in Jacksonville, FL are:
What cities near Jacksonville, FL are hiring for Vector Databases jobs? Cities near Jacksonville, FL with the most Vector Databases job openings:
Contractor
Re-posted 9 days ago
Job description
Local to Cary NC only!
Role Value Proposition:
The position sits within the newly consolidated Data and Analytics (D&A) organization supporting the U.S. Business of MetLife. U.S. D&A assists all business lines of MetLife's U.S. business (about 2/3 of MetLife Global by earnings) with everything related to data, analytics, and data science, from data infrastructure, data governance, data engineering, data modeling, data analysis, to business intelligence, data science, and AI.
The Lead Data Scientist is crucial to DnA USB's Engagement Strategy team, creating Machine Learning and AI solutions to support marketing campaigns and business engagement. You will provide hands-on technical leadership in the design, development, and operation of Machine learning and AI solutions within a regulated, enterprise environment.
You will own technical architecture, solution, and implementation decisions for solutions within a defined business domain, ensuring solutions are scalable, reliable, and compliant with governance and risk standards. You will work closely with the architect, data engineering, platform engineering, DevOps, product, and business stakeholders to translate business requirements into robust AI solutions.
Key Responsibilities:
• Team Leadership: Lead the solution and a team of data scientists delivering AI and ML solution for marketing and business engagement use cases
• Ownership: Accountability for technical decisions, project outcomes, timelines, and production stability within a defined domain.
• Planning and Business alignment: Lead the planning and execution of data science use cases, ensuring alignment with business goals and objectives.
• Model Development: Design, train, and optimize machine learning and deep learning models for a variety of marketing and business engagement use cases
• Data Analysis: Analyze complex data sets to identify trends, patterns, and actionable insights that can inform business strategies.
• Collaboration: Collaborate with stakeholders and cross-functional teams to develop and implement data-driven solutions.
• Platform Integration: Enable seamless integration of AI capabilities into business applications and workflows through APIs, SDKs, and microservices.
• Stakeholder Communication: Visualize data, create reports, and present findings to senior management and cross-functional teams.
• Develop statistical models, analytics, and Machine Learning algorithms using Python and cloud tools (Azure).
• Research and Innovation: Stay up to date with the latest advances in AI, Data Science, and Machine Learning.
• ML-Ops Best Practices: Optimize platform components for efficiency, scalability, and reliability using best practices in distributed computing, resource management, and cloud-native architectures.
Essential Business Experience and Technical Skills:
Required:
• Bachelor's or master's degree in computer science, Data Science, Engineering, Mathematics, or a related field.
• 8+ years of overall experience in AI/ML engineering and/or data science.
• 5+ years of insurance business and/or financial industry experience with sales, marketing, and/or customer engagement analytics.
• Proven experience designing, deploying, and operating production ML and/ or GenAI solutions, including APIs, batch, and real-time inference.
• Experience in developing Machine Learning models using Python (preferably in the cloud)
• Familiarity with best practices for responsible AI, including data privacy, bias mitigation, and/or model monitoring.
• Strong SQL knowledge and data analysis skills for data anomaly detection and Exploratory Data Analysis.
• Experience with Dominos, Power BI, and/or Azure ML
• Statistical Knowledge: A strong understanding of statistics and mathematics is essential for data analysis and prediction.
• Use predictive modeling or AI solutions to increase and optimize customer experience/communication, revenue generation, ad targeting, and other business outcomes
• Very good presentation skills to present results clearly and effectively by creating presentations with storytelling, visualizations & results
• Very good problem solver and excellent communication skills - both written and verbal
Preferred:
• Experience with employee benefits plans is a plus
• Hands-on experience with cloud platforms (Azure/Databricks).
• Hands-on expertise with Retrieval-Augmented Generation (RAG) architectures, including integrating external data sources and vector databases to enhance LLM outputs.
• Strong understanding of prompt engineering, fine-tuning, and evaluation of generative models for real-world applications.
Ability to build, optimize, and scale GenAI pipelines for tasks such as document Q&A, summarization, chatbots, and knowledge retrieval.
Role Descriptions: Digital : Data Science
Essential Skills: Digital : Data Science
Desirable Skills:
Keyword:
Skills: Digital : Data Science
Experience Required: 4-6
Role Value Proposition:
The position sits within the newly consolidated Data and Analytics (D&A) organization supporting the U.S. Business of MetLife. U.S. D&A assists all business lines of MetLife's U.S. business (about 2/3 of MetLife Global by earnings) with everything related to data, analytics, and data science, from data infrastructure, data governance, data engineering, data modeling, data analysis, to business intelligence, data science, and AI.
The Lead Data Scientist is crucial to DnA USB's Engagement Strategy team, creating Machine Learning and AI solutions to support marketing campaigns and business engagement. You will provide hands-on technical leadership in the design, development, and operation of Machine learning and AI solutions within a regulated, enterprise environment.
You will own technical architecture, solution, and implementation decisions for solutions within a defined business domain, ensuring solutions are scalable, reliable, and compliant with governance and risk standards. You will work closely with the architect, data engineering, platform engineering, DevOps, product, and business stakeholders to translate business requirements into robust AI solutions.
Key Responsibilities:
• Team Leadership: Lead the solution and a team of data scientists delivering AI and ML solution for marketing and business engagement use cases
• Ownership: Accountability for technical decisions, project outcomes, timelines, and production stability within a defined domain.
• Planning and Business alignment: Lead the planning and execution of data science use cases, ensuring alignment with business goals and objectives.
• Model Development: Design, train, and optimize machine learning and deep learning models for a variety of marketing and business engagement use cases
• Data Analysis: Analyze complex data sets to identify trends, patterns, and actionable insights that can inform business strategies.
• Collaboration: Collaborate with stakeholders and cross-functional teams to develop and implement data-driven solutions.
• Platform Integration: Enable seamless integration of AI capabilities into business applications and workflows through APIs, SDKs, and microservices.
• Stakeholder Communication: Visualize data, create reports, and present findings to senior management and cross-functional teams.
• Develop statistical models, analytics, and Machine Learning algorithms using Python and cloud tools (Azure).
• Research and Innovation: Stay up to date with the latest advances in AI, Data Science, and Machine Learning.
• ML-Ops Best Practices: Optimize platform components for efficiency, scalability, and reliability using best practices in distributed computing, resource management, and cloud-native architectures.
Essential Business Experience and Technical Skills:
Required:
• Bachelor's or master's degree in computer science, Data Science, Engineering, Mathematics, or a related field.
• 8+ years of overall experience in AI/ML engineering and/or data science.
• 5+ years of insurance business and/or financial industry experience with sales, marketing, and/or customer engagement analytics.
• Proven experience designing, deploying, and operating production ML and/ or GenAI solutions, including APIs, batch, and real-time inference.
• Experience in developing Machine Learning models using Python (preferably in the cloud)
• Familiarity with best practices for responsible AI, including data privacy, bias mitigation, and/or model monitoring.
• Strong SQL knowledge and data analysis skills for data anomaly detection and Exploratory Data Analysis.
• Experience with Dominos, Power BI, and/or Azure ML
• Statistical Knowledge: A strong understanding of statistics and mathematics is essential for data analysis and prediction.
• Use predictive modeling or AI solutions to increase and optimize customer experience/communication, revenue generation, ad targeting, and other business outcomes
• Very good presentation skills to present results clearly and effectively by creating presentations with storytelling, visualizations & results
• Very good problem solver and excellent communication skills - both written and verbal
Preferred:
• Experience with employee benefits plans is a plus
• Hands-on experience with cloud platforms (Azure/Databricks).
• Hands-on expertise with Retrieval-Augmented Generation (RAG) architectures, including integrating external data sources and vector databases to enhance LLM outputs.
• Strong understanding of prompt engineering, fine-tuning, and evaluation of generative models for real-world applications.
Ability to build, optimize, and scale GenAI pipelines for tasks such as document Q&A, summarization, chatbots, and knowledge retrieval.
Role Descriptions: Digital : Data Science
Essential Skills: Digital : Data Science
Desirable Skills:
Keyword:
Skills: Digital : Data Science
Experience Required: 4-6
About Real Soft
Sourced by ZipRecruiter
Industry
It services
Company size
501 - 1,000 Employees
Headquarters location
Monmouth Junction, NJ, US
Year founded
1991