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Retrieval Augmented Generation Jobs in Arizona (NOW HIRING)

Build and maintain content vectorization and retrieval-augmented generation (RAG) pipelines that give AI agents access to relevant financial context, including prior work product, regulatory guidance ...

AI Engineer III - Agentic AI

Phoenix, AZ · On-site

$103K - $174K/yr

Help implement and maintain retrieval-augmented generation (RAG) pipelines over financial data, with an emphasis on correctness and safety. * Contribute to shared AI infrastructure such as LLM ...

Integrate with large language models (LLMs) using prompt engineering, fine-tuning, and retrieval-augmented generation (RAG) techniques. * Implement MCP client and server within the Grafana ecosystem ...

Design, build and rollout production-grade AI solutions, including LLM-powered applications, retrieval-augmented generation (RAG) systems, ensuring high standards of accuracy, reliability, and ...

Collaborate with ML engineers to deploy and scale AI models, specifically focusing on RAG (Retrieval-Augmented Generation) workflows. ? Infrastructure & DevOps: Own the lifecycle of your services ...

... Retrieval Augmented Generation (RAG) using vector search and enterprise knowledge sources. • Continuously optimize prompts for accuracy, consistency, latency, and cost efficiency. • Build and ...

AI Engineering Leader

Tempe, AZ · On-site

$98K - $129K/yr

Multi-agent architectures Retrieval-Augmented Generation (RAG) pipelines GraphRAG implementations Autonomous agent workflows and orchestration Develop and integrate AI agents with tools, APIs, and ...

The AI Platform Architect designs and implements the architecture that connects Troon's data platform to AI-enabled applications, including retrieval-augmented generation (RAG) services, document and ...

The AI Platform Architect designs and implements the architecture that connects Troon's data platform to AI-enabled applications, including retrieval-augmented generation (RAG) services, document and ...

Showing results 21-40

Retrieval Augmented Generation information

What does a retrieval augmented generation engineer do?

A Retrieval Augmented Generation engineer typically spends their day designing and implementing systems that combine information retrieval with advanced generative models, such as large language models. This includes fine-tuning models, integrating external data sources, developing vector search pipelines, and evaluating output quality. Collaboration with data scientists, machine learning engineers, and product teams is common to ensure the solutions meet user requirements and scale effectively. Additionally, RAG engineers often troubleshoot issues, monitor model performance in production, and stay informed about the latest advancements in AI and information retrieval.

What is a retrieval augmented generation?

A Retrieval Augmented Generation (RAG) job typically involves developing and optimizing AI systems that enhance text generation by incorporating external knowledge retrieved from relevant sources. Professionals in this field work on integrating retrieval mechanisms with large language models to improve the relevance, accuracy, and factual grounding of generated content. Common responsibilities include designing retrieval systems, fine-tuning language models, optimizing performance, and ensuring the seamless integration of factual data into AI-generated text. This role is highly interdisciplinary, involving expertise in natural language processing (NLP), machine learning, and information retrieval.

What skills and qualifications are needed for retrieval augmented generation?

To thrive in a Retrieval Augmented Generation (RAG) engineering role, you need a solid background in machine learning, natural language processing (NLP), and experience with scalable information retrieval systems, typically supported by a relevant degree in computer science or a related field. Familiarity with tools such as Python, PyTorch or TensorFlow, vector databases, and search platforms like Elasticsearch is essential, along with practical experience deploying and tuning RAG pipelines. Strong problem-solving skills, a collaborative mindset, and effective communication abilities set outstanding professionals apart in this field. These competencies are crucial for designing, implementing, and optimizing hybrid retrieval-generation AI systems that address complex, real-world information needs.

What are the most commonly searched types of Retrieval Augmented Generation jobs in Arizona? The most popular types of Retrieval Augmented Generation jobs in Arizona are:
What job categories do people searching Retrieval Augmented Generation jobs in Arizona look for? The top searched job categories for Retrieval Augmented Generation jobs in Arizona are:
What cities in Arizona are hiring for Retrieval Augmented Generation jobs? Cities in Arizona with the most Retrieval Augmented Generation job openings:
Infographic showing various Retrieval Augmented Generation job openings in Arizona as of August 2026, with employment types broken down into 45% Full Time, and 55% Contract. Highlights an 74% In-person, and 26% Remote job distribution.

Staff Software Engineer

Equity Methods

Scottsdale, AZ

Full-time

Re-posted 28 days ago


Job description

Staff Software Engineer

Equity Methods is seeking a Staff Software Engineer to join our Technology and Innovation group. This is a senior individual contributor role for an engineer who thrives at the intersection of financial services domainexpertiseand applied AI engineering. You will work directly alongside our Python-based financial consultants to analyze how complex equity compensation and valuation workflowsoperatetoday - and redesign them as agentic AI workflows with well-placed Human-in-the-Loop (HITL) checkpoints that preserve the rigor and judgment our clients depend on.

This is not a product or infrastructure role. The core of the job is deep collaboration with practitioners who understand the domain, translating that understanding into production AI systems that augment, not replace, expert judgment.

The Role in a Nutshell

  • Embed with financial consulting teams to conduct structured workflow analysis and requirements gathering, mapping how SAS and Python-based financial processes actually work before designing any solution.
  • Identify where AI agents can take over routine, high-volume, or pattern-driven steps in financial workflows, and where human review, approval, or override is non-negotiable.
  • Design and implement agentic AI workflows using Amazon Bedrock and N8N, with HITL checkpoints that route exceptions, edge cases, and judgment-dependent decisions to the appropriate human expert.
  • Build and maintain content vectorization and retrieval-augmented generation (RAG) pipelines that give AI agents access to relevant financial context, including prior work product, regulatory guidance, and client-specific parameters, at runtime.
  • Develop backend services in Python on AWS, with Lambda or FastAPI as the primary compute layers, backed by Postgres, Snowflake and DynamoDB for workflow state management and audit logging of agent decisions, human overrides, and process outcomes.
  • Containerize and deploy agentic services using Docker, maintaining clean separation between workflow components and ensuring deployments are observable, reproducible, and rollback-safe.
  • Build lightweight React interfaces where consultants need to review, approve, or override AI-generated outputs as part of HITL steps.
  • Collaborate with the AI Product Manager on scoping and prioritization, and with the Distinguished Software Engineer on architecture decisions that span multiple workflow systems.
  • Hold yourself accountable for adoption, not just delivery. Measure impact by whether consultants are actually using what you built, whether it saves them time, and whether they come back asking for more.
  • Participate in production ownership of deployed agentic workflows: monitoring agent behavior, catching drift or failure modes, and iterating quickly.

We Are:

  • Zealous about exceptional client service and internal collaboration.
  • Agile and execution-focused , with a bias toward action and impact.
  • Growth-oriented and committed to professional development.
  • Feedback-heavy and mentoring-rich , with a culture of continuous improvement.
  • Eager to solve complex, ambiguous problems with creativity and rigor.
  • Hardworking and passionate about building the future of technology-enabled consulting.

Qualifications & Requirements:

  • 8 - 12 years of software engineering experience, with meaningful time spent consulting, working directly with domain experts or business practitioners rather than within a pure product engineering organization.
  • Demonstrated ability to conduct structured requirements gathering and workflow analysis with non-technical stakeholders - producing precise technical specifications without requiring an intermediary.
  • Desire to learn to design and implement agentic AI systems, including multi-step agent orchestration, tool use, and Human-in-the-Loop workflow design.
  • Understanding of LLMs in a production context: prompt engineering, retrieval-augmented generation (RAG), and content vectorization using embedding models (e.g., Amazon Titan, Knowledge Base, PGVector, Elasticsearch, or equivalent).
  • Strong proficiency in backend service development. Python familiarity is a plus but not required. Expertise in at least one of these languages: Python, Java, Kotlin, Groovy.
  • Hands-on experience with AWS Lambda as a primary compute pattern, along with DynamoDB, API Gateway, S3, and IAM. Familiarity with Amazon Bedrock or equivalent managed LLM infrastructure is preferred.
  • Working knowledge of N8N, UI Path or comparable workflow automation tooling; willingness to become the internal subject-matter expert in N8N or selected workflow platform is expected.
  • Working knowledge of React is sufficient to build consultant-facing HITL review and approval interfaces.
  • Comfort operating in an environment where the problem definition evolves as domain understanding deepens - judgment and communication matter as much as execution speed. MBA or Finance degree helpful but not required.
  • Background check required.

About Equity Methods

Equity Methods is a financial services consulting firm specializing in stock-based equity compensation and other complex financial reporting and valuation services. We deliver impact-rich engagements across three core practice groups: financial reporting, valuation services, and HR advisory.

With over 125 professionals and experience with over 1,000 publicly traded clients (including 50 Fortune 100 companies), Equity Methods combines the best traits of an industry-leading professional services firm with the best of an entrepreneurial, technology-enabled company. We have consistently been rated a Top Company to Work for in Arizona .

Employment Type: Full-Time