Capstone Integrated Solutions

Machine Learning Engineer

• Participate in data discovery workshops to inventory source systems including property management platforms, marketing channels, and CRM data, and translate findings into data lake architecture requirements.
• Design and implement a multi-zone enterprise data lake on Amazon S3 (raw, conformed, enriched, aggregated) with ingest, cleansing, and business layers aligned to the SOW architecture.
• Build batch and streaming data ingestion pipelines using AWS Glue, Amazon Kinesis, and AWS Data Pipeline across CDP, marketing, and property management data sources.
• Implement data transformation and orchestration frameworks using AWS Glue ETL and AWS Step Functions, including AWS Glue Data Catalog for metadata management and discovery.
• Configure Amazon Athena for serverless SQL querying across the data lake; support QuickSight integration with curated data sets for business analytics.
• Develop and deploy ML models on Amazon SageMaker for lead scoring, predictive maintenance, intelligent underwriting risk scoring, and AI-powered audience segmentation.
• Integrate Amazon Bedrock foundation models to enable generative AI capabilities including customer profile enrichment, hyper-personalization, and intelligent marketing automation.
• Use Kiro CLI to accelerate AI-assisted development workflows, spec-driven pipeline implementation, and automated code generation tasks.
• Design and implement entity resolution pipelines using Amazon Entity Resolution to identify, deduplicate, and merge customer records into unified golden records.
• Implement real-time and batch data synchronization pipelines between source systems and the Customer Data Platform (CDP).
• Support Azure data lake migration: conduct discovery, assess schemas and transformation logic, provision AWS target environments, execute migration via AWS DataSync, and perform data validation and reconciliation.
• Implement data lake security using AWS Lake Formation, including row-level security and column-level encryption.
• Build and maintain data models to support Customer 360 views, ML feature stores, and executive analytics dashboards.
• Ensure data quality, validation, and integrity across all pipeline stages and ML model outputs; support UAT for data-dependent features.
• Collaborate with Full Stack, DevOps/MLOps, and AWS engagement teams; contribute to architecture documentation, pipeline runbooks, and data governance documentation.

Senior Data/ML Engineer – AWS

Business Analyst

• Partner with business stakeholders to document, validate, and refine MFP business processes and workflows.
• Lead and facilitate User Acceptance Testing (UAT): write test scripts, coordinate testing sessions, track defects, and drive resolution.
• Develop and deliver end-user training materials including job aids, quick reference guides, and instructor-led training sessions.
• Create and maintain use cases, business requirements documentation, and functional specifications aligned with the MFP implementation.
• Act as a subject-matter resource to end users during go-live and hypercare periods, answering functional questions and escalating issues appropriately.
• Collaborate closely with technical consultants and vendors to translate business needs into system configuration requirements.
• Support change management activities including stakeholder communication, impact assessments, and readiness reviews.
• Facilitate working sessions and workshops with planning, merchandising, and finance teams to gather requirements and validate solution design.
• Assist with data validation and reconciliation to ensure planning outputs align with business expectations.
• Contribute to future-phase demand forecasting implementation as the engagement progresses (Board / Tulio).

Functional Business Analyst

Full-stack Engineer

• Design and develop the application architecture, UI/UX, and technical requirements during the Discovery & Design phase.
• Build and deploy a document upload portal enabling subcontractors to submit insurance certificates, bonding letters, and financial statements.
• Develop RESTful and serverless APIs using AWS Lambda and Amazon API Gateway to support frontend/backend integration.
• Integrate AI-powered document validation and exception management workflows driven by Amazon Bedrock.
• Develop and host frontend applications using AWS Amplify and Amazon S3.
• Participate in CI/CD pipeline setup, automated testing, and deployment to production environments.
• Collaborate with cross-functional team members including AI/ML, DevOps, and Data Engineering resources.
• Conduct integration testing, support user acceptance testing (UAT), and develop user training materials.
• Contribute to knowledge transfer documentation and post-deployment operations guides.

Senior Data/ML Engineer – AWS

Functional Business Analyst

Senior Full Stack Developer