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Ai Rag Jobs in New York (NOW HIRING)

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 ...

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 ...

Snowflake Solution Architect

Manhattan, NY · On-site

$69.50 - $91.50/hr

The ideal candidate will have hands-on experience with Snowflake Cortex AI (RAG, Semantic Search, and Multi-Agent Systems) , enterprise data architecture, and cloud-based analytics solutions. This ...

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Ai Rag information

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.
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What cities in New York are hiring for Ai Rag jobs? Cities in New York with the most Ai Rag job openings:

AI Architect (Agentic AI / RAG)

CLPS Global

Manhattan, NY • On-site

Other

Posted 3 days ago

New


Job description

Job Description:
Role - AI Architect (Agentic AI / RAG)
Location - NY and NJ (Onsite - Locals Preferred)
Mode of Interview: Both Video and in-person
Employment Type: Full-time
Experience: 15+ years
 
Key Responsibilities:
  • Agentic AI architecture; spec-driven development; agent harness design and evaluation; multi-agent orchestration patterns; Retrieval-Augmented Reasoning; semantic enrichment and knowledge graph integration; prompt engineering; identity-as-code and policy-as-code; design-time and runtime governance; cloud platforms (AWS and Google Cloud Platform); enterprise security architecture patterns.
  • End-to-end agentic AI architecture, design-to-code conversion, security-platform architecture patterns, hands-on review across AWS and Google Cloud Platform, alignment with enterprise standards.
  • Build the supervisor, intake, and data-source-level agents; implement React-pattern reasoning loops; design and tune the retrieval pipeline; lead prompt and evaluation engineering.
  • Own architecture and diagrams; collaborate closely with internal cross-functional teams; pair with key client-side technical stakeholders on design decisions; oversee design-to-code conversion and testing; chair architecture review forums.