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Vector Databases Jobs in Boca Raton, FL (NOW HIRING)

Strong understanding of modern agent development patterns including RAG, vector search, prompt engineering, tool/function calling, and frameworks such as LangChain, LangGraph, or LlamaIndex. * Deep ...

Senior Data & AI Engineer

Boca Raton, FL

$100K - $136K/yr

Strong understanding of modern agent development patterns including RAG, vector search, prompt engineering, tool/function calling, and frameworks such as LangChain, LangGraph, or LlamaIndex. * Deep ...

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Vector Databases information

What is the salary of a vector database developer?

The salary of a vector database developer typically ranges from $80,000 to $150,000 annually, depending on experience, location, and company size. Skilled developers with expertise in machine learning, data structures, and database management may earn higher salaries, especially in tech hubs or with advanced certifications.

Are vector databases the future?

Vector database jobs involve managing and optimizing databases designed for high-dimensional vector data, which are essential for AI and machine learning applications. As AI continues to grow, demand for professionals skilled in vector database technologies and related tools like embedding models is expected to increase, making this a promising field for future job opportunities.

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 can you do with a vector database?

A vector database is used in roles involving data management and machine learning to store, search, and retrieve high-dimensional vector representations of data such as images, text, or audio. It enables efficient similarity searches, supporting applications like recommendation systems, natural language processing, and computer vision. Working with a vector database often requires knowledge of data structures, indexing techniques, and programming skills in languages like Python or C++.

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 the top 5 vector databases?

Top vector databases used in data management and AI applications include Pinecone, Weaviate, FAISS, Milvus, and Annoy. These databases are optimized for storing and searching high-dimensional vector data, often requiring skills in machine learning and database management. They are widely adopted for tasks like similarity search and recommendation systems.
What are popular job titles related to Vector Databases jobs in Boca Raton, FL? For Vector Databases jobs in Boca Raton, FL, the most frequently searched job titles are:
What cities near Boca Raton, FL are hiring for Vector Databases jobs? Cities near Boca Raton, FL with the most Vector Databases job openings:
Senior AI Engineer - Agentic Systems

Senior AI Engineer - Agentic Systems

DPR Construction

Fort Lauderdale, FL • On-site

$99K - $137K/yr

Full-time

Posted 9 days ago


DPR Construction rating

7.8

Company rating: 7.8 out of 10

Based on 35 frontline employees who took The Breakroom Quiz

25th of 79 rated construction


Job description

Job Description

Senior Agentic AI Engineer
Join a dynamic and fast-evolving team that is building next-generation AI-based tools and agent systemsfor the construction Industry. Our AI and Data Team is focused on designing intelligent AI agents, andcopilots using modern AI/ML techniques.


You will work closely with cross-functional teams, including business stakeholders, data engineers, andtechnical leads, to ensure alignment between business needs and data architecture and define datamodels for specific focus areas.
What you'll work on


Build end-to-end Gen AI solutions - develop, refine, and implement advanced Gen AI models andensure the success delivery of projects
Develop agents over our construction data estate, systems that answer non-trivial questions, takemulti-step action against APIs and databases, and operate under governance constraints that matter.
Tool-use and orchestration design in LangGraph: defining the right granularity of tools, the right statemachines, and the right human-in-the-loop checkpoints for a domain where wrong answers have real-world consequences.
Evaluation infrastructure for non-deterministic systems: building harnesses, golden datasets, andregression tests that let us ship agentic features with confidence. We treat eval as a first-classengineering problem, not an afterthought.
Retrieval and knowledge architecture spanning Snowflake Cortex, vector search, and structuredgraphs over our project data. You'll make real decisions about when retrieval is the answer and whenit isn't.
Integration with our domain systems: partnering with engineers and analysts working on safety,operations, scheduling, and risk to turn agentic capabilities into tools superintendents and PMs use.
Technical direction-setting across the Agentic AI track: design reviews, architectural guidance, raisingthe bar on what "production-ready" means for agents, and mentoring engineers earlier in theiragentic AI journey.
Collaborate with stakeholders, presenting findings to a non-technical audience and providing strategicrecommendations.
Ensure the scalability, reliability, and security of AI solutions by implementing best practices for AImodel development, deployment, and maintenance.
Required Experience


6+ years of production software engineering, with at least 2 years building LLM-powered systems in aproduction setting.
Demonstrated experience designing and shipping agentic systems using LangChain and LangGraph orcomparable frameworks.
Strong Python engineering fundamentals: testing, packaging, performance, and the parts of the stackthat aren't glamorous.
Practical experience with retrieval architectures (vector stores, hybrid search, reranking) and with atleast one major cloud data platform.
Track record of evaluation work, you can describe specific eval systems you've built and what theycaught that ad-hoc testing missed.
Excellent written and verbal communication, with experience presenting technical work to non-technical stakeholders.

Bonus


Snowflake and Snowflake Cortex (Cortex Search, AI_COMPLETE, Cortex Analyst).
Experience with knowledge graphs or graph-augmented retrieval.
Familiarity with construction, AEC, or other physical-industry domains.
Experience working under AI governance frameworks like model risk, responsible AI, intake processes.
Open-source contributions to the LangChain/LangGraph ecosystem or related agentic tooling.

DPR Construction is a forward-thinking, self-performing general contractor specializing in technically complex and sustainable projects for the advanced technology, life sciences, healthcare, higher education and commercial markets. Founded in 1990, DPR is a great story of entrepreneurial success as a private, employee-owned company that has grown into a multi-billion-dollar family of companies with offices around the world.


Working at DPR, you'll have the chance to try new things, explore paths and shape your future. Here, we build opportunity together-by harnessing our talents, enabling curiosity and pursuing our collective ambition to make the best ideas happen. We are proud to be recognized as a great place to work by our talented teammates and leading news organizations like U.S. News and World Report, Forbes, Fast Company and Newsweek.


Explore our open opportunities atwww.dpr.com/careers.


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