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Ai Rag Jobs in Avon, CT (NOW HIRING)

Infosys

AI Engineer

Hartford, CT · On-site

TensorFlow, PyTorch, LangChain • Expertise In Generative AI, Including RAG (Retrieval-Augmented Generation), AI Agents / Agentic frameworks, Prompt Engineering • Bachelor's degree or foreign ...

ClifyX

GEN AI Data Scientist Engineer

Hartford, CT · On-site

... RAG and Knowledge-Based AI Solutions • Intermediate experience with Cloud Platform (GCP, AWS) and hands-on cloud experience • Intermediate experience with MLOps, DevOps, and Deployment • Agile ...

Infosys Limited Digital

AI Engineer

Hartford, CT · On-site

$115K - $138K/yr

RAG (Retrieval-Augmented Generation) AI Agents / Agentic frameworks Prompt Engineering Proficiency with Microsoft Azure AI ecosystem Preferred Skill and Experience Experience building Copilot or AI ...

Digital United

AI Solutions Associate

Farmington, CT · Remote

$60K - $75K/yr

CT, FL, GA, IA, MA, MD, NC, OH, RI, SC, TN Primacy is seeking an AI Solutions Associate to support ... Preferred: exposure to cloud platforms, APIs, LLM integrations, prompt engineering, RAG workflows ...

The Hartford

AVP Applied AI

Hartford, CT · On-site +1

Ensure evaluation frameworks span classification, information retrieval, RAG/chat, forecasting, and ... Accountable for portfolio-level AI governance ensuring alignment with Legal, Compliance, Model Risk ...

Digital United

AI Solutions Associate

Farmington, CT · On-site +1

$60K - $75K/yr

CT, FL, GA, IA, MA, MD, NC, OH, RI, SC, TN Primacy is seeking an AI Solutions Associate to support ... Preferred: exposure to cloud platforms, APIs, LLM integrations, prompt engineering, RAG workflows ...

Infosys Limited Digital

AI Engineer

Hartford, CT · On-site

$115K - $138K/yr

RAG (Retrieval-Augmented Generation) AI Agents / Agentic frameworks Prompt Engineering Preferred Skill and Experience Experience in Data Engineering & pipelines (ETL/ELT, streaming, batch processing ...

Propark Mobility

AI Automation Specialist

Hartford, CT · Hybrid

$70K - $80K/yr

Shipped at least one of these yourself: a retrieval (RAG) system, an LLM-powered tool, or a working ... AI coding tools already in your daily workflow (Claude Code, Cursor, Codex, Github Copilot, or ...

Propark Mobility

AI Automation Specialist

Hartford, CT · On-site

$70K - $80K/yr

Shipped at least one of these yourself: a retrieval (RAG) system, an LLM-powered tool, or a working ... AI coding tools already in your daily workflow (Claude Code, Cursor, Codex, Github Copilot, or ...

Propark Mobility

AI Automation Specialist

Hartford, CT · Hybrid

$70K - $80K/yr

Shipped at least one of these yourself: a retrieval (RAG) system, an LLM-powered tool, or a working ... AI coding tools already in your daily workflow (Claude Code, Cursor, Codex, Github Copilot, or ...

Propark Mobility

AI Automation Specialist

Hartford, CT · Hybrid

$70K - $80K/yr

Shipped at least one of these yourself: a retrieval (RAG) system, an LLM-powered tool, or a working ... AI coding tools already in your daily workflow (Claude Code, Cursor, Codex, Github Copilot, or ...

Propark Mobility

AI Automation Specialist

Hartford, CT · Hybrid

$70K - $80K/yr

Shipped at least one of these yourself: a retrieval (RAG) system, an LLM-powered tool, or a working ... AI coding tools already in your daily workflow (Claude Code, Cursor, Codex, Github Copilot, or ...

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Showing results 21-40

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 10, 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 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 job categories do people searching Ai Rag jobs in Avon, CT look for? The top searched job categories for Ai Rag jobs in Avon, CT are:
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 72% Physical, 4% Hybrid, and 24% Remote job distribution, with an average salary of $57,310 per year, or $27.6 per hour.

Senior Software Engineer - Platform & Agentic AI Engineering

The Hartford Financial Services Group, Inc.

Hartford, CT • On-site

$123K - $162K/yr

Full-time

Posted 9 days ago

The Hartford rating

8.8

Company rating: 8.8 out of 10

Based on 121 frontline employees who took The Breakroom Quiz

57th of 304 rated insurance

Job description

Senior Staff Software Engineer - IE07HE
We're determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals - and to help others accomplish theirs, too. Join our team as we help shape the future.
This requisition hires Senior AI Engineers who will:
Design and deliver production-grade Agentic AI systems using Google ADK, Anthropic MCP, LangGraph/LangChain, and modern Agentic protocols. Build secure, scalable AI platform capabilities with strong engineering fundamentals in Python/Typescript, Terraform, and GCP. Enable enterprise adoption of AI by creating reusable frameworks, APIs, and platform capabilities aligned with engineering standards, compliance needs, and modern cloud patterns.
Overview
The Senior AI Engineer will architect, build, and operationalize advanced AI and multi-agent solutions leveraging RAG, GraphRAG, Agentic AI frameworks, and enterprise-grade cloud engineering.
A key requirement is robust, practical experience implementing MCP and ADK Agentic Protocols, with a solid understanding of:
  • Agent memory
  • Session and context lifecycle management
  • Tooling interfaces
  • Secure capability boundaries
  • Permissions and role enforcement

Additionally, candidates must have hands-on experience with AlloyDB's AI/Agentic capabilities-including vector indexing, embedding support, and tight integration with Vertex AI-as well as strong fundamentals in PostgreSQL / Postgres RDS for building retrieval systems, agent memory stores, and structured context-management layers.
The engineer must demonstrate strong foundational engineering skills in Python or Typescript, IaC (Terraform), DevOps pipelines, and secure distributed system design using GCP services such as Vertex AI, Cloud Run, Cloud Storage, and AlloyDB.
The role additionally requires deep, hands-on experience building and extending agent harnesses-the runtime scaffolding that orchestrates the agent execution loop, tool invocation, dynamic context-window assembly, sub-agent delegation, and guardrail and permission enforcement-together with production expertise in LangChain and LangGraph.
Fluency in spec-driven, agentic development frameworks such as GitHub Spec-Kit, OpenSpec, and BMAD-METHOD, used to translate intent into executable specifications and orchestrate AI-assisted delivery at enterprise scale.
Responsibilities
AI/Agentic System Architecture & Development
  • Design and implement Agentic AI solutions using Google ADK, LangGraph, LangChain, and Agent Engine.
  • Build and extend agent harnesses, implementing the agent execution loop, tool-call orchestration, dynamic prompt and context assembly, sub-agent delegation, streaming, token-budget management, and hook and guardrail enforcement.
  • Engineer advanced LangChain and LangGraph orchestration, including LCEL chains, stateful graphs, checkpointing, human-in-the-loop workflows, memory, retrievers, callbacks, and LangSmith tracing and evaluation.
  • Build advanced RAG and GraphRAG pipelines, vector retrieval systems, and knowledge-graph-augmented reasoning.

Implement MCP-compliant agents with capability registration, secure tool invocation, memory storage, and session state management.
  • Apply deep knowledge of Agentic Protocol design (ADK & MCP), such as:
    • Agent memory and conversation state
    • Tool authorization
    • Multi-step workflows and orchestration
    • Session boundary and identity controls
  • Leverage AlloyDB and PostgreSQL/RDS for:
    • Vector storage and hybrid search
    • Agent memory persistence, session management, and state recovery
    • Structured prompt scaffolding and fact retrieval
    • ACID-compliant transactional reasoning layers
  • Develop scalable AI microservices using Python/Typescript, Cloud Run, Vertex AI, and event-driven components.
  • Optimize model inference, retrieval latency, and overall system performance.

Spec-Driven & Agentic Development
  • Drive spec-driven development (SDD) using frameworks such as GitHub Spec-Kit, OpenSpec, and BMAD-METHOD, translating product intent into executable specifications, plans, and agent-ready task breakdowns.
  • Establish specification-first review gates and living change proposals that align human engineers and AI agents before implementation begins.

Security, Governance & Session Management
  • Implement enterprise-grade security for agents including:
    • OAuth and SSO flows
    • IAM roles, service accounts, least-privilege design
    • Secure MCP tool access, command permissioning, and input validation
  • Architect safe session-based AI interactions with proper expiration, auditing, and context isolation.
  • Ensure compliance with enterprise governance, Responsible AI requirements, and platform guardrails.

Platform Engineering, IaC & DevOps
  • Use Terraform to build GCP infrastructure for AI workloads, vector stores, knowledge graphs, and orchestration services.
  • Build CI/CD pipelines for model deployments and agent lifecycle automation.
  • Implement observability, monitoring, and logging for AI service health.

Innovation & Collaboration
  • Evaluate emerging tools and frameworks-including Claude Code, GitHub Copilot, AWS Kiro, GitHub Spec-Kit, OpenSpec, and BMAD-METHOD-and integrate them into engineering workflows.
  • Partner with architects, data engineers, and platform teams to implement cross-domain AI capabilities.
  • Document architecture patterns, reusable code modules, and standards for MCP/Agentic development.

Qualifications
Experience
  • 6-8 years in software engineering, including 2+ years in GenAI, multi-agent, or LLM systems.
  • Proven delivery of at least one production-grade AI or Agentic system, preferably involving RAG or GraphRAG.

Technical Expertise
Core Engineering
  • Strong engineering fundamentals in Python and/or Typescript.

Agentic AI & Protocols
  • Deep, practical experience with:
    • MCP (Model Context Protocol) - tools, capabilities, memory, session orchestration, security
    • Google ADK Agentic Protocols - agents, workflows, context management
    • LangChain & LangGraph - LCEL chains, agents, tools, memory, retrievers, stateful graph orchestration, checkpointing, human-in-the-loop control, and LangSmith tracing and evaluation
    • Agent harness engineering - agent execution loops, tool-call orchestration, context and prompt assembly, sub-agent delegation, streaming, token-budget management, and hook and guardrail enforcement

Spec-Driven & Agentic Development Frameworks
  • Hands-on experience with spec-driven development (SDD) workflows and tooling, including GitHub Spec-Kit (specify, plan, tasks, implement), OpenSpec (change proposals and living specifications), and BMAD-METHOD (agentic planning with specialized agent roles)
  • Proven ability to decompose product intent into executable specifications, structured plans, and agent-ready task breakdowns that align human and AI contributors before code is written
  • Familiarity with greenfield and brownfield delivery driven by multi-agent planning, context engineering, and specification-first review gates

Databases & Agent Memory Stores
  • Hands-on experience with AlloyDB, including:
    • Vector indexing / pgvector
    • AI inference acceleration and Vertex AI integration
    • Building agent memory and retrieval layers
    • Transactional context management for Agentic systems
  • Strong PostgreSQL/Postgres RDS fundamentals, including:
    • Schema design for knowledge retrieval
    • Query optimization
    • Hybrid search patterns
    • Durable storage for AI session and memory state

Cloud & Platform Skills
  • Experience with:
    • Vertex AI (Model Garden, Embeddings, Vector Search, Generative AI APIs)
    • GCP Cloud Run, AlloyDB, Cloud Storage, Secret Manager
    • Terraform / IaC
    • CI/CD automation, containerization, environment provisioning
    • OAuth, SSO, IAM roles/policies, service account management

Additional
  • Experience with AI coding tools (Claude Code, GitHub Copilot, AWS Kiro).
  • Strong understanding of LLM safety, governance, context window management, and prompt engineering.

Preferred Certifications
  • GCP Professional Cloud Architect
  • GCP Professional Machine Learning Engineer

Education
  • Bachelor's or Master's in Computer Science, Engineering, or related field.

This role will have a Hybrid work schedule, with the expectation of working in an office (Columbus, OH, Chicago, IL, Hartford, CT or Charlotte, NC) 3 days a week (Tuesday through Thursday). Candidates must be authorized to work in the US without company sponsorship. The company will not support the STEM OPT I-983 Training Plan endorsement for this position.
Compensation
The listed annualized base pay range is primarily based on analysis of similar positions in the external market. Actual base pay could vary and may be above or below the listed range based on factors including but not limited to performance, proficiency and demonstration of competencies required for the role. The base pay is just one component of The Hartford's total compensation package for employees. Other rewards may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition. The annualized base pay range for this role is:
$127,600 - $191,400
Equal Opportunity Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age
About Us | Our Culture | What It's Like to Work Here | Perks & Benefits

What The Hartford employees say

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About Hartford

Sourced by ZipRecruiter

Hartford Financial Services Group, widely recognized as The Hartford, is a renowned company based in Hartford, CT, US. Established in 1810, it has evolved into an industry leader in the insurance and financial services sector, proudly serving more than one million businesses in the US. The Hartford is committed to offering a gamut of insurance products that include homeowners, automobile, and business insurance as well as employee benefits and mutual funds. The company’s core values revolve around customer-focused innovations, diversity and inclusion, and ethical dealings that have earned them a customer-centric reputation. This shapes their mission which revolves around aiding their clients to overcome unforeseen obstacles and enhancing their wealth over time. Among the company's noted accomplishments is being consistently listed among the World's Most Ethical Companies, a testament to their unwavering commitment towards responsible business practices.

Industry

Finance and insurance

Company size

10,000+ Employees

Headquarters location

Hartford, CT, US

Year founded

1810

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