AI Engineer - Data Success

Who You Are:

As an AI Engineer - Data Success at Citian, you'll support efforts to incorporate AI into our data extraction process with our client datasets.  You will work alongside our Engineering, Data, and Research teams to help build scalable processes for data ingestion and find solutions to improve our data integrity.  You are resourceful, innovative, and continuously seek opportunities to improve processes and drive efficiency. Your strong communication and collaboration skills enable you to work effectively with cross-functional teams and stakeholders. 

Expected Duties:

• Evaluate whether AI outputs meet real-world infrastructure standards and work with our technical data teams accordingly.
• Help lead our efforts to get models to perform how we want them to, including enabling models with tool use and other techniques to get the best out of the LLMs.
• Support AI image recognition and computer vision strategies for infrastructure asset data extraction assessing model approaches, overseeing implementation, and validating outputs against domain benchmarks.
• Contribute to fine-tuning strategy for transportation/infrastructure-specific models by collaborating with the research team on domain adaptation, benchmarking, and model evaluation.
• Evaluate and recommend third-party AI tools, APIs, and foundation models relevant to Citian's use cases, balancing build vs. buy tradeoffs in coordination with Engineering.
• Own and manage the application of AI to QA/QC processes; directing strategy, evaluating outputs for domain accuracy, and iterating with the Data team to continuously improve data quality and integrity.

Required Skills & Experience:

• Bachelor's in Computer Science, Data Science, or related field
• 3+ years of professional experience within Data-focused roles and 1+ year of true AI focused experience ideally within a product environment
• Understanding of Python/SQL code and ability to retrieve and store data safely through a back end
• Familiarity with RESTful APIs and back-end concepts sufficient to prototype, test, and evaluate integrations. Hands-on implementation experience a plus but not required.
• Experience with cloud services, specifically AWS, and the ability to leverage its features for scalable back-end AI solutions is highly desired. (S3, Lambda Functions)
• Experience with computer vision or AI image recognition applications, even at a prototyping or evaluation level is preferred
• Exposure to civil engineering, transportation infrastructure, or geospatial data, sufficient to critically evaluate AI outputs in these domains and communicate credibly with domain experts.