M/L Data Engineer - Python & C

Start Date: Interview TypesSkills Python , Snowflake, .. Visa Types Green Card, US Citiz..ackfill Role - Open for Relocation Candidates as well.Position Title: M/L Data Engineer - Python & Computer VisionLocation: Mclean, VADuration: 6-12 Months ContractJob Description:True must-haves:Strong, recent, hands-on Python codingSQL and SnowflakeImage processing or computer-vision output experienceOpenCV or Pillow; PyTorch/TensorFlow exposure is relevantJSON and semi-structured dataData modeling and data engineeringValidation of extracted images, attributes, and metadata against source documentsRoot-cause analysis and ability to fix extraction, metadata, and output issuesMortgage, appraisal, collateral, or closely related financial-domain experienceAbility to work onsite in McLean for a short-term contractImportant clarification:The resource will not primarily build or train computer-vision models. The person will:Prepare model inputs and process model outputsExtract property images from appraisal PDFsGenerate and validate attributes and metadataAssociate images with the correct property and loanLoad or support data in AWS S3, Snowflake, and a vector databaseCompare extracted information with original source documentsInvestigate discrepancies and determine root causesFix extraction, metadata, and downstream-data problemsPartner with modelers when the problem is inside the modelWork mainly with data-engineering and modeling teamsNotebook experimentation and model-training experience are advantageous but secondary.Best candidate profile:Production-level PythonOpenCV/image extractionSQL, Snowflake, JSON, and AWS S3.Model-output validation and reconciliation.Mortgage/appraisal data knowledge.Fannie Mae or Freddie Mac experience.Some AI or computer-vision model familiarity.Strong expertise in Python, SQL, Image analytics, data modeling, and snowflake. Financial / mortgage background is required.Position Overview:We are seeking a hands-on Data Scientist with strong Python and Computer Vision skills to design, build, test, and operationalize capabilities that convert image-based content and model outputs into usable, reviewable, and analytics-ready data products. This role requires strong software engineering fundamentals, computer vision coding experience, data engineering skills, and the ability to partner across product, modeling, engineering, research, and business teams. The Data Scientist will contribute to capabilities for image extraction, metadata generation, model-output validation, quality review enablement, and downstream structured data integration. The role is expected to balance Python development, testing, analytical troubleshooting, and delivery execution to help users review, validate, and act on computer vision outputs.Key Responsibilities:Computer Vision Development & Model Output EngineeringDevelop, enhance, and maintain code that supports computer vision model output processing, image extraction, metadata generation, and validation workflowsWork with image-based model outputs, bounding boxes, labels, confidence scores, extracted attributes, and structured metadata to support downstream review and analysisBuild reusable utilities for parsing, transforming, validating, and comparing computer vision outputs across model versions and production-style runsApply strong Python coding practices to automate testing, issue detection, data preparation, and model-output quality checksProduct & QC Workflow EnablementSupport product capabilities that allow users to review, validate, correct, and quality check model-generated outputsTranslate computer vision models output into user-facing review patterns, QC screens, exception workflows, and validation experiencesPartner with UI developers, product owners, and business users to define practical capabilities for model-output inspection and operational reviewTest Data, Validation & Quality EngineeringCreate and manage representative test datasets for image extraction, metadata validation, regression testing, and model performance reviewPerform structured testing of model runs across historical and current datasets to identify extraction gaps, metadata issues, formatting errors, and quality concernsValidate extracted images, image classifications, and metadata against original PDFs, appraisal reports, and other authoritative source documents to confirm completeness, accuracy, and traceabilityDocument defects with clear evidence, expected results, actual results. Retest remediated issues and contribute to repeatable quality gates for model-output readinessData Integration, JSON Engineering & Analytics Readiness Develop scripts and data pipelines that convert model outputs into structured and semi-structured formats suitable for research, analytics, and downstream consumptionSupport loading and validation of model outputs as JSON Variant or similar semi-structured data formatsEnsure extracted image attributes, metadata, and model-output payloads are traceable, consistent, and accessible for analysisCross-Functional Delivery & Technical Problem SolvingCollaborate across product management, model development, UI engineering, data engineering, research, business, and delivery teams to operationalize computer vision capabilities within data-driven productsInvestigate technical issues across image inputs, model outputs, metadata payloads, data loads, and user-facing QC workflowsCommunicate progress, risks, blockers, and technical findings clearly to engineering partners and business stakeholdersRequired QualificationsComputer Vision Coding & Software Engineering Hands-on experience coding computer vision or image-processing solutions using Python and common libraries such as OpenCV, Pillow, PyTorch, TensorFlow, or similar frameworksStrong ability to process image files, extracted labels, model predictions, confidence scores, annotations, bounding boxes, and metadata payloadsExperience writing modular, maintainable code for automation, validation, transformation, testing, and troubleshooting