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Ai Rag Jobs in Dover, NJ (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 ...

TMS

Senior Agentic AI Engineer

Iselin, NJ · On-site

$106K - $145K/yr

Role: Senior AI Software Engineer (Agentic AI / AI Agents) Location: Iselin, NJ Duration: 12 ... Develop reusable agent frameworks, SDKs, evaluation pipelines, RAG, and memory systems * Own ...

New

Judge Group, Inc.

Agentic AI Architects

Edison, NJ · On-site

$155K - $200K/yr

Prompt Engineering & RAG * Architect solutions leveraging advanced prompt engineering techniques. * Design Retrieval-Augmented Generation (RAG) frameworks to enhance accuracy and reliability of AI ...

New

Northern Base

Gen AI Copilot Lead

Edison, NJ · On-site

Retrieval-Augmented Generation (RAG) * Vector Databases * LLM Evaluation & Optimization * Python ... AI Solution Architecture & Design * Responsible AI, Governance & Security Practices Preferred ...

New

Sibitalent Corp

Generative AI Engineer

Whippany, NJ · On-site

Design and implement RAG pipelines, vector search solutions, and embedding based retrieval systems. * Build scalable AI services using Python, FastAPI/Flask, and cloud platforms (Azure/AWS/GCP)

Accord Technologies Inc.

Senior AI Engineer

Piscataway, NJ · On-site

$106K - $146K/yr

Design and implement solutions involving Large Language Models (LLMs), embeddings, vector databases, Retrieval-Augmented Generation (RAG), and prompt engineering. * Work with cloud AI services such ...

StatusNeo Inc.

AI Engineer

Parsippany Troy Hills, NJ · On-site

Build scalable Retrieval-Augmented Generation (RAG) pipelines using enterprise knowledge sources. * Develop AI agents and workflow automation using LangChain, LangGraph, or similar orchestration ...

IPolarity LLC

Generative AI Engineer

Whippany, NJ · On-site

$70/hr

RAG (Retrieval-Augmented Generation) * Workflow Automation & Integrations * Strong Communication and Stakeholder Engagement Preferred Skills * AI Black Belt certification/expertise * Firewall, Data ...

Alpha Silicon

Senior AI Solution Architect

Raritan, NJ · On-site

Lead the architecture and design of end-to-end Gen AI solutions , including LLM integration, Retrieval Augmented Generation (RAG), fine-tuning, and prompt engineering. * Define scalable architectures ...

Accord Technologies Inc.

Lead AI Engineer

Piscataway, NJ · On-site

$104K - $137K/yr

RAG Pipeline Engineering (SageMaker + Lambda) * MCP Server Development & Tool Integrations * Experience building agentic workflows or autonomous AI agents * Exposure to code generation / code fixing ...

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Ai Rag information

See Dover, NJ salary details

$32.8K

$59.7K

$85.6K

How much do ai rag jobs pay per year?

As of Aug 8, 2026, the average yearly pay for ai rag in Dover, NJ is $59,676.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,200.00 and $66,600.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an AI researcher?

To thrive as an AI Researcher, you need a strong background in computer science, mathematics, and machine learning, usually with an advanced degree such as a Master's or Ph.D. Proficiency with programming languages like Python, deep learning frameworks (e.g., TensorFlow, PyTorch), and familiarity with scientific research tools is essential. Critical thinking, creativity, and effective collaboration are vital soft skills for generating novel ideas and working in multidisciplinary teams. These skills and qualities are crucial to drive innovation and solve complex problems in the rapidly evolving field of artificial intelligence.

What is the difference between Ai Rag vs Data Analyst?

AspectAi RagData Analyst
Required CredentialsTypically a diploma or certification in AI, machine learning, or related fieldsBachelor's degree in statistics, mathematics, or related fields
Work EnvironmentTech companies, AI startups, research labsBusiness, finance, healthcare, and various industries
Employer & Industry UsagePrimarily in AI development and researchAcross industries for data interpretation and decision-making
Common Search & ComparisonYesYes

Ai Rag and Data Analyst roles share overlapping skills in data handling and analysis, but Ai Rag focuses more on AI-specific applications and machine learning, while Data Analysts concentrate on interpreting data to inform business decisions. Both roles are vital in data-driven industries, with Ai Rag often working in AI development environments and Data Analysts supporting strategic insights across sectors.

What is an AI RAG?

AI RAGs, or Retrieval-Augmented Generation systems, are a type of artificial intelligence that combines the power of retrieving information from large databases or documents with generating human-like text responses. This approach allows AI models to provide more accurate, up-to-date, and contextually relevant answers by referencing external data sources during the generation process. RAGs are commonly used in applications like chatbots, search engines, and customer support systems, where comprehensive and factual responses are important.

What are common challenges faced by AI RAG engineers when integrating retrieval systems with large language models?

AI RAG engineers often encounter challenges such as ensuring seamless integration between retrieval systems and language models, maintaining low latency for real-time responses, and handling the quality and relevance of retrieved data. Additionally, tuning the system to balance retrieval accuracy with generative fluency can be complex, especially when dealing with large or unstructured datasets. Collaboration with data engineers, ML researchers, and product teams is essential to address these challenges and optimize system performance.
What cities near Dover, NJ are hiring for Ai Rag jobs? Cities near Dover, NJ with the most Ai Rag job openings:
Ai Rag jobs near you

Agentic AI Developer

Hirekeyz Inc

Berkeley Heights, NJ • On-site

Full-time

Re-posted 4 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).