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Ai Rag Jobs (NOW HIRING)

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 ...

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 ...

Futran Tech Solutions Pvt. Ltd.

AI Architect

AI Architect Location: Austin, TX (Remote) Required Skills & Competencies * Proven experience ... Strong understanding of LLMs, prompt engineering, RAG patterns , and GenAI workflow design.

Intellectt INC

AI Software Engineer

Foster City, CA · On-site

RAG Architecture & Vector Databases * AI Agents & Conversational AI * LangChain / LlamaIndex / AutoGen * Backend & API Development * Cloud Technologies (AWS/GCP/Azure) * Docker / Kubernetes ...

ReturnPro

AI/ML Engineer

Aventura, FL · On-site

We are expanding our AI/ML capabilities to include generative AI-driven solutions, RAG applications, and predictive models for retail pricing using collected data from multiple sources. We are ...

Precision Technologies Corp

AI Engineer

Dallas, TX · On-site

Contract Key Skills - AI, Python, Rag, LLM Overview We are seeking an AI Engineer with proven experience in building and scaling AI-powered applications . This role combines hands-on development with ...

ReturnPro

AI/ML Engineer

Aventura, FL · On-site

We are expanding our AI/ML capabilities to include generative AI-driven solutions, RAG applications, and predictive models for retail pricing using collected data from multiple sources. We are ...

Spruce Infotech

Agentic Gen AI Engineer

Alpharetta, GA · On-site

Key Responsibilities • Proficiency in technologies like Agentic AI, Gen AI, RAG, Python, Lang Graph, Lang Chain • Design, build, and deploy agentic AI systems using generative AI models, agent ...

Digital Links Inc

AI/ML Engineer

Minneapolis, MN · On-site

This role requires strong expertise in Generative AI, RAG (Retrieval-Augmented Generation), and enterprise integrations. The ideal candidate should be capable of independently delivering scalable AI ...

Digital Links Inc

AI Solution Architect

Princeton, NJ · On-site

$66 - $87/hr

Deep understanding of AI/ML concepts, including natural language processing (NLP), deep learning, generative AI, RAG architectures, and MLOps practices. Cloud Proficiency: Experience with cloud ...

East West Bank

AI Governance

Manhattan, NY · On-site

Leading governance processes related to LLM, Generative AI, RAG, intelligent automation, and advanced analytics solutions. * Evaluating AI technologies, platforms, and vendors to ensure alignment ...

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Ai Rag information

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$32K

$58.2K

$83.5K

How much do ai rag jobs pay per year?

As of Jun 24, 2026, the average yearly pay for ai rag in the United States is $58,245.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,000.00 and $65,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an AI Researcher, and why are they important?

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 are AI RAGs?

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 some common challenges faced by AI RAG (Retrieval-Augmented Generation) 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.
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Ai Rag jobs near you
Infographic showing various Ai Rag job openings in the United States as of June 2026, with employment types broken down into 100% Full Time. Highlights an 66% Physical, 4% Hybrid, and 30% Remote job distribution, with an average salary of $58,245 per year, or $28 per hour.

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

Envision Technology Solutions

Berkeley Heights, NJ • On-site

$52.50 - $72.25/hr

Other

Posted 3 days ago

Job description

Dear Application,

Please let me know if you are interested.

Title: Agentic AI Developer (Python) - Vertex AI RAG + Graph/Vector Datastores

Location: Berkeley Heights, NJ (5 Days Onsite)

Hire Type: Long Term Contract

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 Google Cloud Platform 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).

Apply