AI Engineer
- Closed
- US Company | Medium ( employees)
- LATAM (100% remote)
- 3+ years
- Long-term · 40h/week
- Enterprise software
- Full Remote
Required skills
- Python
- SQL
- REST APIs
- AWS Lambda
- AI Technologies
- LLM
- AI Agents
- Agentic Workflows
- ETL
Requirements
Must-haves
- 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
Nice-to-haves
- 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
What you will work on
- 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