AI Engineer

Required skills

Requirements

3+ years of software or data engineering experience
Experience building AI features in production software: LLM integrations, AI agents, agentic workflows
Experience with Python
Experience with SQL
Experience building data pipelines and ETL processes
Experience working with datasets and data processing
Experience shipping AI features into existing products, not only greenfield projects
Experience with RESTful APIs and back-end concepts sufficient to prototype, test, and evaluate integrations
Ability to retrieve and store data safely through a back-end
Deep knowledge of core computer science topics (e.g., optimization, algorithms, etc.)
Strong communication skills in both spoken and written English

Startup experience
Experience 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 RAG and vector databases (e.g., Pinecone, Weaviate, etc.)
Experience with computer vision or AI image recognition, including at a prototyping level
Experience with cloud services, particularly AWS (e.g., S3, Lambda, etc.)
Proficiency with prompt engineering
Exposure to civil engineering, transportation infrastructure, or geospatial data to critically evaluate AI outputs in these domains and communicate credibly with domain experts
Bachelor's Degree in Computer Engineering, Computer Science, or equivalent

Build AI features into our existing products, creating AI workflows that improve automation across data extraction and processing
Own the application of AI to QA/QC processes, setting strategy and iterating with the Data team to strengthen data quality and integrity
Integrate LLMs and AI agents into production software, applying tool use and other techniques to get the most out of the models
Work with the Data team on datasets for evaluation, in-context learning (ICL), and related applications
Act as the domain bridge between civil/transportation engineering knowledge and AI capabilities, judging whether model outputs meet real-world infrastructure standards
Direct AI image recognition and computer vision approaches for infrastructure asset data extraction, validating outputs against domain benchmarks
Assess third-party AI tools, APIs, and foundation models against our use cases, weighing build vs. buy tradeoffs with Engineering
Partner with the Research team on prompting techniques, model capabilities, and domain adaptation
Prototype concepts and turn technical findings into actionable product decisions
Collaborate with the Engineering, Data, and Research teams to build repeatable processes

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