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Rag Engineer Jobs (NOW HIRING)

Symhas

Gen AI Lead - RAG Specialist

Pleasanton, CA · On-site

Gen AI Lead - RAG (Retrieval-Augmented Generation) Specialist We are looking for a highly skilled ... Guide a team of engineers in building scalable, production-grade knowledge systems * Partner with ...

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Rag Engineer information

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$59.5K

$90.5K

$153.5K

How much do rag engineer jobs pay per year?

As of Jun 28, 2026, the average yearly pay for rag engineer in the United States is $90,511.00, according to ZipRecruiter salary data. Most workers in this role earn between $68,500.00 and $105,000.00 per year, depending on experience, location, and employer.

How to become a RAG engineer?

A RAG (Red, Amber, Green) engineer typically works in risk assessment or project management, requiring a background in engineering, data analysis, or related fields. Developing skills in data visualization tools, risk management methodologies, and obtaining relevant certifications can enhance qualifications for this role.

What is the difference between Rag Engineer vs Textile Technician?

AspectRag EngineerTextile Technician
Required CredentialsEngineering degree, technical certificationsDiploma or degree in textiles or related field
Work EnvironmentFactories, manufacturing plants, R&D labsTextile mills, production facilities, quality control labs
Industry UsageDesigning and improving rag production processesMonitoring textile quality, testing fabrics

While both roles involve working within the textile industry, a Rag Engineer primarily focuses on the engineering aspects of rag production, process optimization, and machinery, whereas a Textile Technician concentrates on fabric testing, quality control, and ensuring textile standards are met. The roles often overlap in industry settings but differ in technical focus and responsibilities.

Which 3 jobs will survive AI?

For a Rag Engineer, jobs that require complex manual skills, problem-solving, and hands-on work are more likely to survive AI automation. These include roles such as skilled trades like welding or machining, specialized maintenance technicians, and quality control inspectors. Such positions often depend on physical dexterity, judgment, and adaptability that AI and automation are less capable of replicating fully.

What is a $900,000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as AI research director, senior machine learning engineer, or AI executive, often requiring advanced skills in data science, programming, and deep learning. These roles usually involve leadership, strategic planning, and extensive experience, and they may be found in large tech companies or specialized AI firms.

What engineers make $500,000?

Senior engineers in specialized fields such as petroleum, aerospace, or software engineering can earn $500,000 or more annually, especially with experience, advanced skills, and in high-demand industries. Executive engineering roles or those with significant leadership responsibilities may also reach this compensation level.
More about Rag Engineer jobs
What cities are hiring for Rag Engineer jobs? Cities with the most Rag Engineer job openings:
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Rag Engineer jobs near you
Infographic showing various Rag Engineer job openings in the United States as of June 2026, with employment types broken down into 96% Full Time, 1% Part Time, and 3% Contract. Highlights an 81% Physical, 4% Hybrid, and 15% Remote job distribution, with an average salary of $90,511 per year, or $43.5 per hour.
Generative AI Applications Engineer (Agents & RAG)

Generative AI Applications Engineer (Agents & RAG)

Accenture Federal Services

Washington, DC

$64.50 - $85.75/hr

Other

Posted 28 days ago

Accenture Federal Services rating

8.4

Company rating: 8.4 out of 10

Based on 19 frontline employees who took The Breakroom Quiz

46th of 430 rated business services

Job description

Build AI that matters. We ship production GenAI apps for confidential federal programs across defense, national security, public safety, civilian, and military health where reliability, privacy, and safety aren't optional. AFS is a technology company within global Accenture and a Glassdoor Top 100 Best Place to Work. You'll join a collaborative, inclusive community with handson growth, certifications, and industry training. We ship in weeks, not quarters, and measure success with latency, reliability, safety, and cost. 

Confidentiality matters: We don't disclose program details publicly. If you advance, we'll share specifics during the process. 

Role Overview 

You'll turn mission needs into secure, reliable, and scalable GenAI applications no model training required. This is a hands-on role across agentic workflows, RAG, prompt/policy design, LLM evaluation, and platform integration. You'll own the end-to-end path from use case evaluation production deployment operational excellence, partnering with product, security, data, and SRE to ship features safely and at scale. 

What You'll Do (Day to Day) 

  • Design & ship mission grade GenAI: Build agentic workflows and RAG systems tailored to mission data and environments; target low hallucination, tight p95 latency, and predictable cost. 
  • Agent frameworks & orchestration: Apply patterns from LangChain/LlamaIndex/Semantic Kernel; design task decomposition, tool use, guardrails, and recovery/fallback strategies. 
  • Platform integration (no model training): Implement with AWS Bedrock, Azure OpenAI, Google Vertex AI, Amazon Kendra, and managed services (e.g., Document AI, Gemini, Gemma). 
  • LLM selection & evaluation: Compare models for quality, safety, latency, cost; author/test prompts & policies; deploy with observability and safe rollback/fallback. 
  • RAG done right: Build retrieval pipelines & vector search (Pinecone, Weaviate, OpenSearch, pgvector, FAISS/Chroma); handle data prep, chunking, metadata, and IRstyle evals (e.g., NDCG) to maximize signal to noise. 
  • Production rigor: Instrument metrics/logs/traces; run A/B experiments; maintain incident playbooks; and implement safety & compliance guardrails. 
  • SRE & FinOps for AI: Define SLIs/SLOs (quality/latency/safety/cost), run on call and postmortems, reduce MTTR; meter usage and optimize token/spend. 
  • Reusable platform components: Ship SDKs, CI/CD templates, Terraform/IaC modules, evaluation harnesses that accelerate multiple mission team not one-off projects. 
  • Operate in real world constraints: Deliver into hybrid, restricted, or air gapped environments with Zero Trust principles and audit ready controls. 

You'll Thrive Here If you have 

  • End-to-end ownership of production systems: integration deployment observability incident response. 
  • Hands-on experience with LLMs, transformer based apps, and RAG in production. 
  • Strong Python 
  • Experience with vector search and retrieval (Pinecone, Weaviate, OpenSearch, pgvector, FAISS/Chroma) and grounding AI in enterprise/mission data. 
  • U.S. Citizenship 

Nice to Have 

  • Integration with leading cloud AI services or on prem inference stacks  
  • Background in LLM evaluation, prompt authoring/testing, A/B experimentation, and LLM Ops. 
  • Responsible AI expertise (privacy, security, bias, transparency, human in the loop) and data governance. 
  • Experience implementing tool using agents for API integration and external data access. 
  • Containerization & orchestration (Docker, Kubernetes, VMware) and scripting/automation (Linux Bash, PowerShell). 
  • Prior work in regulated/secure environments (e.g., ATO, STIGs, Zero Trust) with fast shipping. 
  • Familiarity with NVIDIA AI Foundations, OpenAI ChatGPT, and AI assisted dev tools (Cursor, Windsurf, Claude). 
  • Contributions to internal frameworks or opensource; mentorship of engineers. 
  • Clear communication with engineers, PMs, and security/compliance stakeholders. 

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