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Ai Rag Jobs in Avon, CT (NOW HIRING)

CGI Inc.

Agentic AI Developer

Hartford, CT · On-site

Define and implement architectures for RAG, agentic, multi-agent, and multimodal systems . Review and guide solution designs to ensure alignment with AI CoE standards and enterprise architecture

Voya Financial, Inc.

AI Principal Engineer

Windsor, CT · On-site

Lead the design, development, and deployment of complex AI solutions, including LLM-based applications, retrieval-augmented generation (RAG) pipelines, and model-driven services. * Own technical ...

FAVARH

AI Principal Engineer

Windsor, CT · On-site +1

Lead the design, development, and deployment of complex AI solutions, including LLM-based applications, retrieval-augmented generation (RAG) pipelines, and model-driven services. * Own technical ...

Deloitte

Google AI Architect

Hartford, CT · On-site

Build RAG and agentic solutions using Vertex AI Vector Search and BigQuery vector; implement context management, retrieval strategies, and observability. * Define end-to-end architectures across data ...

Deloitte

AI Engineer Consultant

Hartford, CT · Hybrid

We are hiring an AI Engineer to build and operate the data, features, and GenAI foundations that ... Implement LLM application patterns including RAG, document ingestion/chunking, embeddings, vector ...

Info Way Solutions

Gen AI Engineer

Hartford, CT · On-site

... RAG). • Strong understanding of NLP, deep learning, and model fine-tuning techniques. • Experience working with MLOps, cloud-based AI deployment (AWS/GCP/Azure), and containerization (Docker ...

The Hartford

Principal AI Engineer

Hartford, CT · On-site +1

At the Hartford, we are seeking a Principal AI Engineer who is responsible for building our AI ... Proficiency in customization techniques across various stages of the RAG pipeline, including model ...

Deloitte

Google AI Lead Architect

Hartford, CT · On-site

$55.75 - $76.50/hr

Build RAG and agentic solutions using Vertex AI Vector Search and BigQuery vector; implement context management, retrieval strategies, and observability. * Define end-to-end architectures across data ...

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All JobsAi Rag JobsAi Rag Jobs in Avon, CT

Ai Rag information

See Avon, CT salary details

$31.5K

$57.3K

$82.2K

How much do ai rag jobs pay per year?

As of Aug 27, 2026, the average yearly pay for ai rag in Avon, CT is $57,310.00, according to ZipRecruiter salary data. Most workers in this role earn between $48,200.00 and $64,000.00 per year, depending on experience, location, and employer.

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 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 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 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 cities near Avon, CT are hiring for Ai Rag jobs?

Cities near Avon, CT with the most Ai Rag job openings:

Ai Rag jobs near you
Infographic showing various Ai Rag job openings in Avon, CT as of August 2026, with employment types broken down into 76% Full Time, 20% Part Time, and 4% Contract. Highlights an 69% Physical, 4% Hybrid, and 27% Remote job distribution, with an average salary of $57,310 per year, or $27.6 per hour.

AI Architect

Newington, CT • On-site

Talent Software Services, Inc
See more jobs at Talent Software Services, Incexternal

Other

Posted 10 days ago

Job description

Title: AI Architect
Duration: 6 months
Location: Remote
Overview

The AI Architect is responsible for designing, developing, and governing enterprise-grade AI solutions that align with business strategy. This role blends deep technical expertise in artificial intelligence, machine learning, data, and cloud architecture with strong product intuition, security awareness, and leadership. The AI Architect ensures that AI initiatives are scalable, ethical, secure, cost-efficient, and integrated into the broader enterprise ecosystem.

Key Responsibilities
  • AI Strategy & Solution Architecture
    • Define and evolve the enterprise AI architecture, ensuring alignment with business, data, and technology strategies.
    • Design scalable, secure, and compliant automation solutions to streamline across the enterprise.
    • Architect end-to-end AI solutions including data engineering, RAG model development, model operations (MLOps), and lifecycle management.
    • Partner with business, product, and engineering teams to translate business problems into appropriate AI/ML approaches.
    • Develop reference architectures and reusable patterns for generative AI, Agentic AI, predictive models, conversational systems, and intelligent automation.
  • Technical Leadership
    • Provide architectural oversight across AI/ML projects to ensure consistency, performance, and maintainability.
    • Evaluate and select AI technologies, frameworks, cloud services, vector databases, LLM orchestration frameworks, and tooling.
    • Support development teams on model selection, training pipelines, prompt engineering, fine-tuning, RAG (Retrieval-Augmented Generation), and evaluation methodologies.
    • Mentor engineers, analysts, and product teams on AI best practices.
  • Data, Integration & Platforms
    • Partner with data architects and engineering to ensure robust data pipelines, governance, feature stores, and architecture.
    • Design secure and performant integration between AI models and enterprise systems (APIs, microservices, events).
  • Governance & Compliance
    • Ensure AI solutions adhere to enterprise security standards, data privacy policies, and regulatory requirements.
    • Implement responsible AI guardrails, fairness checks, explainability frameworks, and monitoring.
    • Develop and maintain automation governance frameworks, documentation, and audit trails.
  • Operations & Optimization
    • Define MLOps / LLMOps standards including CI/CD pipelines, model monitoring, drift detection, observability, and rollback processes.
    • Drive continuous improvement of model performance, cost optimization, and operational efficiency.
    • Establish KPIs, telemetry, and feedback loops for production AI systems.
  • Collaboration & Enablement
    • Partner with IT, compliance, operations, and customer service teams to align automation initiatives with business goals.
    • Mentor and guide developers and analysts to build a center of excellence (CoE) for automation.
Required Qualifications
  • Bachelor’s degree in Computer Science, Engineering, or a related technical field.
  • 5 years of experience in application development, engineering, or solution delivery roles.
  • 1 year of hands-on experience in AI/ML engineering, data science, or AI solution architecture.
  • Strong hands-on experience with machine learning frameworks and LLM platforms (e.g., OpenAI, Azure AI Foundry, Copilot Studio/Agent Builder, or comparable generative AI ecosystems).
  • Deep expertise in cloud platforms, particularly Microsoft Azure, and modern architectural patterns (microservices, event-driven architectures, API-first design).
  • Proficiency in one or more of the following: Python, Azure Machine Learning, or related AI/ML tooling.
  • Experience with MLOps/LLMOps ecosystems, including tools such as MLflow, Kubernetes, LangChain, vector databases, and feature stores.
  • Strong hands-on experience with ML frameworks, LLM platforms - OpenAI, MSFT/Azure Cloud foundry, Copilot Studio Agent builder, low code/no code platforms, and generative AI tools.
  • Background in RAG systems, model fine-tuning, embeddings, vector storage, and retrieval optimization.
Preferred Qualifications
  • Experience with enterprise-wide AI programs or platform buildouts.
  • Strong understanding of data governance, privacy, security, and model risk management.
  • Prior experience with large-scale transformation programs.
Location

This position is Work At Home, offering flexibility and convenience for the right candidate.

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