Ai Engineer

OverviewAs an AI Solutions Specialist, you design, deploy, and maintain ML and LLM-driven automations for our B2B Risk and Research workflows.You own end-to-end data science processes, from data analysis to production pipelines, delivering actionable insights.You’ll work with cross?functional teams to translate business needs into technical solutions and leverage cloud APIs for advanced data enrichment.This role blends modeling with robust data engineering to drive scalable, high?quality AI-enabled reporting.Compensaciones / BeneficiosComprehensive Healthcare Plans30 days of holidays/yearRemote work options 3 months/year or 1 week per quarterMeal benefit with PluxeeRetirement plans with employer matchWell-being resources and allowancesResponsabilidadesOwn the development, deployment, and maintenance of ML models and LLM-based solutions to automate workflows and enhance reportingApply NLP and information retrieval to extract structured info from unstructured textBuild and maintain robust data pipelines for high-volume processing, enrichment, and model training using Python, SQL, and cloud servicesCollaborate cross-functionally with business, product, and engineering teams to translate requirements into technical solutionsLeverage external APIs including LLM APIs for data enrichment or model integration; deploy tools using cloud services (AWS and GCP)Ensure high standards through documentation, validation, and reproducible workflowsRequisitos principalesBachelor’s or Master’s degree in a quantitative field3+ years in a data science or applied ML role with modeling and pipeline experienceStrong Python programming and ML libraries (scikit-learn, XGBoost, TensorFlow, PyTorch)Experience with AI frameworks/SDKs (LangChain, LlamaIndex) and building/deploying AI agentsSQL proficiency and handling large datasets; version control (Git) and reproducible workflowsCloud experience deploying workloads (AWS; S3, Glue, Lambda, Athena) and secure API integration with LLMsStrong problem-solving and attention to detailExcellent communication to non-technical audiencesCross-functional collaborationPythonscikit-learnXGBoost

Requisitos principalesBachelor’s or Master’s degree in a quantitative field 3+ years in a data science or applied ML role with modeling and pipeline experience Strong Python programming and ML libraries (scikit-learn, XGBoost, TensorFlow, PyTorch) Experience with AI frameworks/SDKs (LangChain, LlamaIndex) and building/deploying AI agents SQL proficiency and handling large datasets; version control (Git) and reproducible workflows Cloud experience deploying workloads (AWS; S3, Glue, Lambda, Athena) and secure API integration with LLMs Strong problem-solving and attention to detail Excellent communication to non-technical audiences Cross-functional collaboration Python