> Markdown version of [/jobs/ext/2391270-ai-engineer-dublin-ca-or-usa-remote](https://www.wearedevelopers.com/jobs/ext/2391270-ai-engineer-dublin-ca-or-usa-remote). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Engineer (Dublin, CA or USA Remote) - **Company:** Savvymoney, Inc. - **Location:** Dublin, CA, United States (Remote available) - **Experience:** Starter - **Salary:** $95,000.0 - $110,000.0 - **Contract:** Permanent contract - **Skills:** A/B Testing, Artificial Intelligence, Amazon Web Services, Application Integration Architecture, Audit Trail, Cloud Engineering, Cyber Security, Cursor (Graphical User Interface Elements), DevOps, Python (Programming Language), Software Engineering, Systems Integration, Management of Software Versions, Web Application Frameworks, Data Logging, Data Classification, Large Language Models, Prompt Engineering, Backend, Information Technology, Low Latency, Data Analytics, Machine Learning Operations, Restful APIs, Microservices - **Published:** August 10, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=cc25cadbf645050d ## About the Role * 1-2+ years of professional software engineering experience, with at least 1 year building production AI/ML or LLM-driven applications. * Strong proficiency in Python and modern backend development (RESTful APIs, microservices, cloud-native deployment on AWS). * Hands-on experience with LLMs, prompt engineering, and RAG pipeline design - you have shipped, not just prototyped. * Familiarity with vector stores, embedding models, and retrieval evaluation. * Strong instincts on cost control, latency, and reliability for LLM-backed systems. * Comfortable working cross-functionally with non-engineering teams to scope, build, test, and operate internal tools. Preferred Experience * Fintech, lending, or financial services background. * Prior experience in DevOps, platform engineering, or InfoSec - the integration-and-automation muscle translates directly to LLM-powered internal tools. * Experience working with regulated data (PII, financial data) and the controls that go with it. * Hands-on experience with AWS Bedrock, Anthropic, OpenAI, and one or more enterprise AI gateways. * Bachelor's degree in Computer Science, Engineering, or a related field, or compelling self-taught equivalent. ## Description Reporting to the AI Engineering Lead, the AI Engineer is the engineering capacity of SavvyMoney's newly chartered AI Engineering Team. You write code. You ship internal AI tools. You build the paved roads that other engineers across SavvyMoney use when they integrate AI into their workflows. This is an internal-build role, not a customer-facing product role. You'll work closely with the AI Engineering Lead on adoption, with our Data & Analytics organization on shared infrastructure, and with InfoSec and Legal on governance plumbing. You'll establish the patterns - RAG, agents, evals, observability, cost control - that the rest of the company adopts by default., Internal Automations and Agents * Design, build, and deploy AI-powered workflow tools for business teams (customer success, finance, legal, operations, sales, people, recruiting). * Translate business pain points into agentic workflows using modern frameworks (LangChain, LangGraph, CrewAI, or equivalent) where the pattern fits. * Ship production-grade tools end-to-end: requirements, prototype, deploy, instrument, iterate. Stakeholder Partnership * Sit with the business team that requested a tool, gather the requirements yourself, and write them down before you build. * Run UAT with the requester - they confirm the tool does the job before it ships. * Demo what you built to the team that asked for it, and to the wider engineering group when the pattern is reusable. Reference Architectures and Paved Roads * Define and maintain the reference patterns that engineers across SavvyMoney use when integrating AI: RAG pipelines, agent loops, evals, observability, cost control, and data classification enforcement. * Publish pre-approved patterns and sample code so engineers don't need a fresh Legal or Security review every time. * Own developer experience for AI integration across the company. LLM Gateway and Cost Control * Own the internal LLM gateway: model routing, logging, abuse prevention, prompt-injection mitigation, and cost attribution. * Build cost-per-outcome reporting (FinOps for AI) and partner with the AI Engineering Lead on portfolio-level cost decisions. Eval Harness * Build and operate the internal eval infrastructure so any internal AI use case can be tested before it ships. * Establish offline evaluation datasets and metrics (task success, factuality and groundedness, toxicity, latency, cost-per-task) and run online A/B tests. * Pick eval tooling (Weights & Biases, TruLens, Promptfoo, MLflow, or equivalent) and standardize prompt versioning. Vendor Integrations * When SavvyMoney adopts a new AI tool (Copilot, Cursor, Claude, Glean, Bedrock, or emerging vendors), you own the technical integration with our identity, data, and security stack. * Hold vendors accountable for performance, scalability, and security commitments. Governance Plumbing * Implement DLP integration, audit logging, prompt-injection mitigation, and data-classification enforcement across the AI surface. * Partner with InfoSec and Legal to make the safe path the easy path. Partner Ops Tooling * Extend internal tooling to partner ops use cases where ROI clearly exceeds the cost of a custom build. * Coordinate with the AI Engineering Lead on which partner-facing AI investments graduate from the AI Engineering Team's portfolio into longer-term ownership. ## Related Videos - [Bringing the power of AI to your application.](https://www.wearedevelopers.com/videos/1010-bringing-the-power-of-ai-to-your-application) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [How to Avoid LLM Pitfalls - Mete Atamel and Guillaume Laforge](https://www.wearedevelopers.com/videos/1328-how-to-avoid-llm-pitfalls-mete-atamel-and-guillaume-laforge) - [Coffee with Developers - Maria Apazoglou](https://www.wearedevelopers.com/videos/1209-coffee-with-developers-maria-apazoglou) - [DevOps Maturity Check – a way to balance autonomy and alignment](https://www.wearedevelopers.com/videos/58-devops-maturity-check-a-way-to-balance-autonomy-and-alignment) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [Are Software Engineer Wages Being Pushed Down? A Report on Tech Salaries](https://www.wearedevelopers.com/magazine/417-are-software-engineer-wages-being-pushed-down-a-report-on-tech-salaries) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Fully Remote Software Engineer Jobs](https://www.wearedevelopers.com/magazine/447-fully-remote-software-engineer-jobs)