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Ai Applications Engineer Jobs in Seattle, WA (NOW HIRING)

Uw

AI DevOps Engineer

Seattle, WA · On-site

As a core technical member of the AI Platforms team, the AI DevOps Engineer drives the engineering, deployment, administration, and quality assurance of AI-powered applications and services across ...

Funko

Sr. AI Engineer

Everett, WA · On-site

$115K - $158K/yr

Build AI-powered applications using Large Language Models (LLMs), AI agents, prompt engineering, and modern orchestration frameworks. * Design and implement intelligent workflows, agentic ...

Funko

Sr. AI Engineer

Everett, WA

$115K - $158K/yr

Build AI-powered applications using Large Language Models (LLMs), AI agents, prompt engineering, and modern orchestration frameworks. * Design and implement intelligent workflows, agentic ...

Funko

Sr. AI Engineer

Everett, WA · On-site

$115K - $158K/yr

Build AI-powered applications using Large Language Models (LLMs), AI agents, prompt engineering, and modern orchestration frameworks. * Design and implement intelligent workflows, agentic ...

LHH US

Senior Databricks AI/ML Engineer

Seattle, WA · Remote

$150K - $183K/yr

Sr. Databricks AI/ML Engineer - Remote Location: Remote (must reside in (WA, OR, ID, OH, SC, TX, FL ... Design and develop AI-driven applications leveraging Azure OpenAI services and Databricks

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How much do ai applications engineer jobs pay per year?

As of Aug 21, 2026, the average yearly pay for ai applications engineer in Seattle, WA is $125,978.00, according to ZipRecruiter salary data. Most workers in this role earn between $95,600.00 and $153,600.00 per year, depending on experience, location, and employer.

What is an AI applications engineer?

AI Applications Engineers are professionals who design, develop, and integrate artificial intelligence (AI) solutions into software applications to solve real-world problems. They work closely with data scientists, software engineers, and business stakeholders to build and deploy machine learning models, automate processes, and enhance user experiences. Their responsibilities often include selecting appropriate AI technologies, writing code, testing models, and optimizing performance. AI Applications Engineers play a key role in translating AI research and prototypes into scalable and maintainable products used in industries like healthcare, finance, retail, and more.

What are the key skills and qualifications needed to thrive as an AI applications engineer?

To thrive as an AI Applications Engineer, you need strong programming abilities (Python, Java, or C++), a solid understanding of machine learning algorithms, and a relevant degree in computer science or engineering. Familiarity with AI frameworks (such as TensorFlow or PyTorch), cloud platforms, and data processing tools is typically required, along with certifications in machine learning or AI. Excellent problem-solving, collaboration, and communication skills help you translate business needs into effective AI solutions and work efficiently with cross-functional teams. These skills are critical for building scalable, reliable AI systems that deliver tangible value to organizations.

How does an AI applications engineer typically collaborate with data scientists and software developers on project teams?

As an AI Applications Engineer, you will often serve as a bridge between data scientists, who build and optimize machine learning models, and software developers, who integrate these models into production systems. Collaboration usually involves translating model requirements into scalable application features, ensuring model outputs align with user needs, and troubleshooting technical challenges that arise during deployment. Regular meetings, code reviews, and shared documentation are common practices to keep everyone aligned and ensure seamless integration. This cross-functional teamwork enhances both the technical robustness and usability of AI-powered applications.

What is the difference between Ai Applications Engineer vs Data Scientist?

AspectAi Applications EngineerData Scientist
Required CredentialsBachelor's in CS, Engineering, or related; knowledge of AI/ML toolsBachelor's or higher in CS, Statistics, or related; strong analytical skills
Work EnvironmentDevelops AI solutions, collaborates with engineering teamsAnalyzes data, builds models, interprets results
Employer & Industry UsageTech companies, AI startups, R&D departmentsFinance, healthcare, tech, research institutions

While both roles involve AI and data, Ai Applications Engineers focus on developing and deploying AI solutions in engineering contexts, whereas Data Scientists analyze data to extract insights. The roles often overlap but differ mainly in their primary focus and application environment.

What does an AI applications engineer do?

An AI applications engineer designs, develops, and implements artificial intelligence solutions to solve specific business problems. They work with machine learning models, data processing, and programming tools like Python or TensorFlow, often collaborating with data scientists and software developers to deploy AI systems effectively.

What are popular job titles related to Ai Applications Engineer jobs in Seattle, WA?

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What job categories do people searching Ai Applications Engineer jobs in Seattle, WA look for?

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Cities near Seattle, WA with the most Ai Applications Engineer job openings:

Ai Applications Engineer jobs near you
Infographic showing various Ai Applications Engineer job openings in Seattle, WA as of August 2026, with employment types broken down into 71% Full Time, and 29% Contract. Highlights an 67% In-person, and 33% Remote job distribution, with an average salary of $125,978 per year, or $60.6 per hour.

Generative AI Applications Engineer (Agents & RAG)

Accenture Federal Services

Seattle, WA • On-site

Full-time

Re-posted 21 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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