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Ai Rag Jobs in Nashua, NH (NOW HIRING)

NetBrain

AI Engineer

Burlington, MA · On-site

$150K - $180K/yr

What We Need We're looking for a Senior AI Engineer to design and build production-grade agent and RAG systems that power intelligent, reliable automation across our platform. This role combines ...

BIRDSVUE LLC

Full Stack AI Engineer

Dunstable, MA · Remote

$60 - $70/hr

Develop Retrieval-Augmented Generation (RAG) solutions using embeddings, vector databases, document indexing, and knowledge retrieval systems. * Integrate AI models with enterprise systems, APIs ...

State Street Corporation

AI Developer, AVP

Burlington, MA · On-site

$54.75 - $75.25/hr

Implement RAG pipelines using enterprise data sources and vector databases * Develop and integrate multi-agent systems using MCP servers, APIs, and A2A based tooling * Embed AI capabilities into core ...

State Street

Sr. AI Developer, VP

Burlington, MA · On-site

$59.25 - $78.25/hr

Required : • Strong experience in LLMs, AI orchestration, and distributed systems • Ability to translate emerging AI patterns (e.g., RAG, agent frameworks, MCP integration) into secure, scalable ...

NetBrain

AI Engineer

Burlington, MA · On-site +1

$150K - $180K/yr

... AI Systems ... Design and implement RAG services, including hybrid retrieval, reranking, embeddings, and citation ...

Monotype

AI Architect

Woburn, MA

$69 - $90.75/hr

Guide the implementation of secure and reliable AI capabilities, including LLM access, RAG, agent orchestration, observability, and cost management * Evaluate emerging AI platforms and tools and ...

Motorola

Senior AI Engineer (US Remote)

Waltham, MA · Remote

$180K - $210K/yr

RAG (Retrieval-Augmented Generation) architectures * Agentic frameworks (LangChain, LlamaIndex, or AWS Bedrock Agents) Development Stack: * Python (AI/ML development & data processing) * NodeJS with ...

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Ai Rag information

See Nashua, NH salary details

$32.3K

$58.8K

$84.3K

How much do ai rag jobs pay per year?

As of Aug 9, 2026, the average yearly pay for ai rag in Nashua, NH is $58,831.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,500.00 and $65,700.00 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.
Ai Rag jobs near you

AI Engineer -North Reading, MA***100% Onsite***Immediate Interview-HCL

Inherent Technologies

North Reading, MA • On-site

Other

Re-posted 12 days ago

Job description

Position: AI Engineer

Location: North Reading, MA***Day 1 Onsite***

Duration: 1 Years

Required Qualifications

  • Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, or a related field.
  • 4+ years of experience in AI/ML engineering or AI solutions architecture.
  • Proven track record of building and deploying real-time AI systems into production.
  • Deep understanding of Generative AI, RAG systems, fine-tuning, and AI agent frameworks.
  • Strong architectural expertise in integrating diverse technologies across software engineering environments.
  • Familiarity with AI compliance and data governance requirements across multiple countries.
  • Hands-on experience with AI augmentation tools applicable to enterprise environments.
  • Excellent communication and collaboration skills to engage with diverse teams across engineering, IT, and legal functions.

Preferred Qualifications

  • Experience with modern AI frameworks such as LangChain, LlamaIndex, or other orchestration tools.
  • Background in software engineering productivity or developer experience (DevEx) improvement roles.
  • Knowledge of cloud platforms (AWS, Azure, or Google Cloud Platform) for deploying AI workloads.
  • Familiarity with MLOps or model deployment processes (external use cases preferred).
  • Demonstrated success in introducing AI tools into large-scale organizations.

Rohit Chauhan

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