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Azure Finops Engineer Jobs in Seattle, WA (NOW HIRING)

King County, WA

Cloud Solution Architect

Seattle, WA · On-site

$160K - $203K/yr

Experience defining delivery and release patterns with CI/CD tooling (Azure DevOps, GitHub Actions ... Background in FinOps and cloud cost management, including building cost models and influencing ...

F5, Inc.

Product Manager II

Seattle, WA · On-site

... internal developer relations (DevRel). * Understanding of cloud governance, FinOps, or managing ... Gemini, Azure AI, AWS Bedrock). What Success Looks Like * Maintain a streamlined, secure model ...

Pwc

ServiceNow Deployment- Senior Associate

Seattle, WA · On-site

$77K - $202K/yr

... Azure, GCP) FinOps Certified Practitioner Preferred Knowledge/Skills: Demonstrates extensive-level ... Developing and re-engineering IT processes, capabilities, and controls in a proven and efficient ...

InfluxData

Director of Finance

Seattle, WA · Remote

$180K - $230K/yr

Developers can query and analyze their time-stamped data in real-time to discover, interpret, and ... FinOps & Cloud Infrastructure: Serve as the financial lead for cloud spend, partnering with ...

Microsoft

Director, Product Marketing

Redmond, WA · On-site

... the Azure organization to align vision and integrate with FinOps communities, and manage the ... to developer and knowledge worker AI consumption that can be integrated into Go-To-Market ...

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Azure Finops Engineer information

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$12

$66

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How much do azure finops engineer jobs pay per hour?

As of Aug 22, 2026, the average hourly pay for azure finops engineer in Seattle, WA is $66.46, according to ZipRecruiter salary data. Most workers in this role earn between $60.19 and $74.66 per hour, depending on experience, location, and employer.

What is the difference between Azure Finops Engineer vs Cloud Financial Analyst?

AspectAzure Finops EngineerCloud Financial Analyst
CertificationsAzure certifications, FinOps certificationsFinancial analysis, cloud cost management certifications
Work EnvironmentCloud environments, Azure platform, DevOps teamsFinance departments, IT teams, cloud cost management
Industry UsageTech, cloud service providers, enterprises using AzureFinance, IT, cloud cost optimization across industries

The Azure Finops Engineer focuses on managing and optimizing cloud costs within Azure, combining technical cloud skills with financial management. The Cloud Financial Analyst emphasizes analyzing and controlling cloud expenses from a financial perspective. While both roles involve cloud cost management, the Azure Finops Engineer is more technical and operational, whereas the Cloud Financial Analyst leans towards financial analysis and reporting.

What are popular job titles related to Azure Finops Engineer jobs in Seattle, WA?

For Azure Finops Engineer jobs in Seattle, WA, the most frequently searched job titles are:

What job categories do people searching Azure Finops Engineer jobs in Seattle, WA look for?

The top searched job categories for Azure Finops Engineer jobs in Seattle, WA are:

Azure Finops Engineer jobs near you
Infographic showing various Azure Finops Engineer job openings in Seattle, WA as of August 2026, with employment types broken down into 100% Full Time. Highlights an 64% In-person, 18% Hybrid, and 18% Remote job distribution, with an average salary of $138,243 per year, or $66.5 per hour.

Generative AI Applications Engineer (Agents & RAG)

Accenture Federal Services

Seattle, WA • On-site

Full-time

Re-posted 23 days ago

Accenture Federal Services rating

8.7

Company rating: 8.7 out of 10

Based on 20 frontline employees who took The Breakroom Quiz

48th of 495 rated business services

Job description

Job Summary:
Accenture Federal Services is dedicated to enhancing the capabilities of the US federal government through technology and innovation. The Generative AI Applications Engineer will be responsible for developing secure and scalable GenAI applications, focusing on agentic workflows and RAG systems for various federal missions.
Responsibilities:
• 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.
Qualifications:
Required:
• 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
Preferred:
• 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.
Company:
Accenture Federal Services is a leading US federal services company and subsidiary of Accenture. Founded in 1989, the company is headquartered in Arlington, USA, with a team of 10001+ employees. The company is currently Late Stage.

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