> Markdown version of [/jobs/ext/3109740-ai-engineer](https://www.wearedevelopers.com/jobs/ext/3109740-ai-engineer). 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 - **Company:** UNITED STATES POSTAL SERVICE - **Location:** New York, NY, United States - **Salary:** $95,000.0 - $140,000.0 - **Contract:** Permanent contract - **Skills:** Sql Data Warehouse, Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Cloud Computing, Continuous Integration, Information Engineering, Data Governance, Data Infrastructure, Data Systems, Decision Support Systems, Python (Programming Language), Machine Learning, SQL Databases, ReactJS, Large Language Models, Snowflake, Multi-Agent Systems, Prompt Engineering, Backend, Git, AI Platforms, Machine Learning Operations, Front End Software Development, Data Pipelines - **Published:** September 27, 2026 - **Apply:** https://www.juju.com/job/16_90539d3a5 ## About the Role Core: * Experience building and deploying AI/ML solutions end to end * Strong Python skills across AI/ML, data engineering, and backend services * Hands-on experience with LLMs - agent orchestration, prompt engineering, RAG architecture, document extraction pipelines * Familiarity with cloud AI services (AWS Bedrock preferred) Also valuable: * SQL and data modeling experience (Snowflake, dbt) * Front-end development with React * Experience working directly with non-technical stakeholders We welcome applicants from a variety of educational and professional backgrounds. If you've built and shipped AI solutions and are looking to own projects end to end, we'd like to hear from you - even if your experience doesn't match every bullet. What We Value * Intellectual curiosity and a passion for solving complex problems. * Balancing technical quality with practical business impact. * An entrepreneurial mindset and creative approach to innovation and continuous improvement. ## Description * Large Language Models (via AWS Bedrock) for document extraction, analysis, and autonomous decision support * RAG architecture, prompt engineering, and agent-based workflows * Python as the backbone for everything: AI pipelines, automation, backend services, and data engineering Data Platform * Snowflake as our cloud data warehouse * DBT for modular, version-controlled data transformations * Airflow for orchestration and workflow automation * SQL for data modeling and analysis' Infrastructure & Delivery * AWS for cloud infrastructure and services * React for front-end interfaces and internal tools * Git for version control and CI/CD About Your Team You'll join a small, collaborative team that builds the AI and data systems powering Monticello's real estate finance operations. What you'll own: * Designing and building AI pipelines that extract structured data from unstructured documents - loan files, appraisals, financial statements, invoices * Building full-stack internal tools (React + Python APIs) that put AI capabilities directly in front of business users * Developing RAG systems, AI agents, and automated workflows that reduce manual work across asset management You'll also contribute to: * Data pipelines and warehouse architecture (Snowflake, Airflow, dbt) * Data quality frameworks and automated QC processes * Dashboards and reporting that drive portfolio decisions ## Related Videos - [Hiring AI Native Talents](https://www.wearedevelopers.com/videos/100268-hiring-ai-native-talents) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Fully Orchestrating Databricks from Airflow](https://www.wearedevelopers.com/videos/336-fully-orchestrating-databricks-from-airflow) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [What is Software Engineering in the Age of AI?](https://www.wearedevelopers.com/magazine/640-what-is-software-engineering-in-the-age-of-ai) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere)