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Vector Ai Jobs (NOW HIRING)

Hirekeyz Inc

Agentic AI Developer

Berkeley Heights, NJ · On-site

Berkeley Heights, NJ Duraction: Full Time Agentic AI Developer (Python) -- Vertex AI RAG + Graph/Vector Datastores Role summary We're looking for a strong agentic AI developer who can build and ...

MphasiS Corporation USA

Agentic AI Lead

Berkeley Heights, NJ · On-site

$146K - $179K/yr

Agentic AI Lead (Python) Vertex AI RAG + Graph/Vector Datastores Berkeley Heights, NJ Role summary We re looking for a strong agentic AI developer who can build and productionize Vertex AI based RAG ...

VIS IT Solutions Inc

AI Architect

Miami, FL · On-site

Miami, FL Duration: 6 months GBaMS ReqID: 10641871 JD: • 10-15 years in AI, ML, or enterprise architecture roles. • Proven track record architecting RAG systems, vector search, and LLM-based ...

Brillius

AI Architect

Miami, FL · On-site

Job Title AI Architect Estimated Start Date ASAP Location Miami, FL Duration(Months) 6 months ... vector search, and LLM-based knowledge platforms. • Strong hands-on experience with: o Python o ...

Falconer AI

Founding AI Engineer

San Francisco, CA · On-site

$200K - $300K/yr

We're looking for a Founding AI Engineer to help us build an AI-powered knowledge platform that ... Research and apply best practices in terms of Knowledge Graph, embeddings, vector and graph RAG ...

MDAEdge

Generative AI Engineer

Austin, TX · On-site

MDAEdge is a company focusing on innovative AI solutions, and they are seeking a Generative AI ... Responsibilities : • Develop pipelines to parse documents, chunk, vectorise, and store vector ...

Rapid Eagle Inc

AI Engineer

Las Vegas, NV · On-site

$60 - $70/hr

Develop RAG (Retrieval-Augmented Generation) applications using vector databases. * Build REST APIs and integrate AI services with enterprise applications. * Design AI agents, chatbots, and ...

Programmers.io

Azure AI/ML Engineer

Bellevue, WA · On-site

$62 - $77/hr

AI,C#,ASP.NET 4.5,Java,LLM,Python,Vector,Semantic Kernel, AI Foundry ,LLMs (Azure OpenAI, OpenAI, Anthropic,MicroServices,Azure DevOps,Docker,Kubernetes Role Descriptions: 8 years of Strong ...

Texas State Library and Archives Commision

AI Engineer

$107K - $146K/yr

Work with vector databases, embeddings, and knowledge retrieval systems. Optimize AI systems for performance, latency, and cost efficiency. Collaborate with cross-functional teams to translate ...

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How much do vector ai jobs pay per hour?

As of Jul 13, 2026, the average hourly pay for vector ai in the United States is $15.16, according to ZipRecruiter salary data. Most workers in this role earn between $9.62 and $17.55 per hour, depending on experience, location, and employer.

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

To thrive as a Vector AI Engineer, you need strong foundations in mathematics, machine learning, and computer science, often supported by a degree in a related field. Expertise with vector databases (such as Pinecone or FAISS), programming languages like Python, and knowledge of frameworks like TensorFlow or PyTorch are typically required. Excellent problem-solving, analytical thinking, and effective communication skills help you translate complex business requirements into scalable AI solutions. These qualifications are crucial for developing, deploying, and maintaining efficient AI systems that leverage vector search and representation for real-world applications.

What are some common challenges faced by professionals working in Vector AI roles, and how can they be addressed?

Professionals in Vector AI roles often face challenges such as managing large-scale, high-dimensional data, ensuring model scalability, and optimizing search algorithms for speed and accuracy. Collaborating closely with data engineers, software developers, and product managers is crucial to integrate AI vector solutions effectively into products. Staying updated on the latest advancements in vector databases and similarity search techniques can also be demanding, so continuous learning and participation in relevant communities are highly beneficial. Adopting best practices for model evaluation and experiment tracking can help address these challenges and drive project success.

What is the difference between Vector Ai vs Data Analyst?

AspectVector AiData Analyst
Required CredentialsTechnical certifications, programming skillsDegree in statistics, data science, or related field
Work EnvironmentTech companies, AI development teamsBusiness, finance, healthcare sectors
Industry UsageAI, machine learning, software developmentData interpretation, reporting, decision support

Vector Ai professionals focus on developing and implementing AI algorithms, requiring technical skills and programming knowledge. Data Analysts interpret data to inform business decisions, often working with statistical tools. While both roles handle data, Vector Ai is more specialized in AI technology, whereas Data Analysts focus on data insights and reporting.

What is a Vector AI and what do they do?

Vector AI typically refers to professionals or technologies focused on vector-based artificial intelligence, which involves the use of high-dimensional vectors to represent data and perform machine learning tasks. These experts work on algorithms that process and analyze vector data for applications like image recognition, natural language processing, and recommendation systems. Their work is crucial in making AI systems more efficient at understanding complex patterns in large datasets. In some contexts, 'Vector AI' may also refer to companies or platforms developing such technologies.
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Vector Ai jobs near you

Agentic AI Developer

Hirekeyz Inc

Berkeley Heights, NJ • On-site

Full-time

Re-posted 9 days ago

Job description

Role: Agentic AI Developer
 
Location: Berkeley Heights, NJ
 
Duraction: Full Time
 

Agentic AI Developer (Python) — Vertex AI RAG + Graph/Vector Datastores

Role summary

We’re looking for a strong agentic AI developer who can build and productionize Vertex AI–based RAG systems (Vertex AI Search / Vertex AI RAG patterns), design reliable tool-using agents, and work comfortably with vector databases and graph databases. You’ll own end-to-end delivery: ingestion → retrieval → agent orchestration → evaluation → deployment.

What you’ll do

  • Design and implement RAG pipelines on Google Cloud / Vertex AI (chunking, embeddings, indexing, retrieval, reranking, grounding).
  • Build agentic workflows (tool use, planning, reflection/guardrails, structured outputs) using Python-first frameworks.
  • Integrate agents with Graph DBs (e.g., Neo4j, JanusGraph, Neptune) and Vector DBs (e.g., Vertex Vector Search, Pinecone, Weaviate, Milvus, pgvector).
  • Create robust data ingestion/ETL from PDFs, docs, webpages, and internal sources; implement metadata strategy and access control.
  • Define and run evaluation (retrieval metrics, answer quality, hallucination/grounding checks), and improve system quality iteratively.
  • Ship to production: APIs, monitoring/observability, cost/performance optimization, CI/CD, and security best practices.

Must-have skills

  • Strong Python (clean architecture, async, testing, typing, packaging).
  • Proven experience building RAG solutions (hybrid search, reranking, chunking strategies, embeddings, prompt + schema design).
  • Hands-on with Vertex AI and GCP fundamentals (IAM, logging/monitoring, Cloud Run/GKE, storage).
  • Experience with at least one agentic framework (e.g., LangGraph/LangChain, LlamaIndex, Semantic Kernel, AutoGen) and tool/function calling patterns.
  • Solid knowledge of vector search concepts and at least one vector DB in production.
  • Comfortable with graph data modeling and graph querying (Cypher/Gremlin/SPARQL basics).
  • Strong engineering practices: code reviews, testing, telemetry, secure-by-design, reliability mindset.

Nice-to-have

  • Knowledge graphs for RAG (entity linking, graph traversal + retrieval fusion).
  • Streaming/messaging (Pub/Sub, Kafka), document pipelines (Document AI), and multilingual retrieval.
  • Experience with evaluation tooling (RAGAS, TruLens, custom eval harnesses), prompt/version management.
  • Frontend integration (basic React/Next.js) or platform enablement (internal developer tooling).