Summary
✨ AI‑Generated
A production-focused AI engineer will build and integrate intelligent capabilities into existing software products. The role combines Python, LLM integrations, AI agents, RAG, data engineering, SQL, cloud data platforms, APIs, and secure data handling, with strong emphasis on shipping reliable AI features.
Highlights
Production-focused AI engineering role working with LLMs, AI agents, RAG, data pipelines, and modern data platforms. The role emphasizes shipping AI capabilities into real products and offers exposure to advanced agentic workflows.
Description
🇨🇴 🇲🇽 Exclusive to candidates based in Colombia and Mexico
Requirements
Must-haves
3+ years of software or data engineering experienceExperience building AI features in production software: LLM integrations, AI agents, RAG, agentic workflowsExperience with PythonExperience with SnowflakeExperience with SQL, PostgreSQLExperience building data pipelines and ETL processesExperience working with datasets and data processingExperience shipping AI features into existing products, not only greenfield projectsExperience with RESTful APIs and back-end concepts sufficient to prototype, test, and evaluate integrationsAbility to retrieve and store data safely through a back-endDeep knowledge of core computer science topics (e.g., optimization, algorithms, etc.)Strong communication skills in both spoken and written English
Nice-to-haves
Startup experienceExperience with LLM APIs and foundation model providers (e.g., OpenAI, Claude, etc.)Experience with agentic workflows and AI agent frameworks (e.g., LangChain, etc.)Experience with DatabricksExperience with RAG and vector databases (e.g., Pinecone, Weaviate, etc.)Experience with computer vision or AI image recognition, including at a prototyping levelExperience with cloud services, particularly AWS (e.g., S3, Lambda, etc.)Proficiency with prompt engineeringExposure to civil engineering, transportation infrastructure, or geospatial data to critically evaluate AI outputs in these domains and communicate credibly with domain expertsBachelor's Degree in Computer Engineering, Computer Science, or equivalent
What you will work on
Build AI features into our existing products, creating AI workflows that improve automation across data extraction and processingOwn the application of AI to QA/QC processes, setting strategy and iterating with the Data team to strengthen data quality and integrityIntegrate LLMs and AI agents into production software, applying tool use and other techniques to get the most out of the modelsWork with the Data team on datasets for evaluation, in-context learning (ICL), and related applicationsAct as the domain bridge between civil/transportation engineering knowledge and AI capabilities, judging whether model outputs meet real-world infrastructure standardsDirect AI image recognition and computer vision approaches for infrastructure asset data extraction, validating outputs against domain benchmarksAssess third-party AI tools, APIs, and foundation models against our use cases, weighing build vs.
buy tradeoffs with EngineeringPartner with the Research team on prompting techniques, model capabilities, and domain adaptationPrototype concepts and turn technical findings into actionable product decisionsCollaborate with the Engineering, Data, and Research teams to build repeatable processes
