Candidate – AI Product Engineer

SUMMARY

Results-driven AI Product Engineer with extensive experience designing and deploying enterprise-grade Generative AI and data solutions across healthcare, retail, and manufacturing domains. Proven expertise in building secure, scalable, and compliant AI systems, including Retrieval-Augmented Generation (RAG) architectures, LLM-powered assistants, and intelligent automation platforms that significantly reduce operational workload. Strong background in cloud-native data engineering, ETL pipeline design, analytics enablement, and KPI-driven decision support. Demonstrated ability to translate complex business and IT requirements into production-ready AI applications, particularly within HIPAA-regulated environments. Recognized for leading end-to-end solution delivery, mentoring technical teams, and collaborating with stakeholders to drive measurable efficiency, reliability, and business impact.

SKILLS

• Generative AI & Agentic AI: Retrieval-Augmented Generation (RAG), LLM Integration, Prompt Engineering, Semantic Search, Vector Databases, Planning & Reasoning Agents, AI Assistants, Intelligent Automation.
• Cloud & Architecture: AWS (Lambda, EC2, S3, DynamoDB, API Gateway, OpenSearch, Bedrock, VPC, IAM, KMS), Azure (Data Factory, Logic Apps), Serverless Architectures, Secure Cloud Deployments.
• Data Engineering & Analytics: ETL Pipelines, Data Modeling, Data Warehousing, Analytics Pipelines, KPI Tracking, Operational Analytics, Log & Interaction Analysis, Dashboard Enablement.
• Programming & Development: Python, SQL, PowerShell, HTML, CSS, REST APIs, Full-Stack AI Application Development, Streamlit.
• Automation & Integration: Workflow Automation, Low-Code/No-Code Platforms, API Integrations, Process Optimization, IT Support Automation.
• AI & Data Platforms: Databricks Lakehouse, Delta Lake, OpenSearch Vector Search, Hugging Face Embeddings, Google AI Studio.
• Security & Compliance: HIPAA Compliance, Data Encryption, Role-Based Access Control (RBAC), Secure AI Design, Cloud Security Best Practices.
• Visualization & Reporting: Power BI (DAX, DirectQuery, Embedded Analytics), Python Visualization Libraries, Executive Dashboards.
• DevOps & Collaboration: Git, GitHub Actions, Docker, CI/CD, Agile & Scrum, SDLC, Cross-Functional Collaboration, Stakeholder Communication.
• Leadership & Delivery: Technical Mentorship, Solution Architecture, GenAI Playbook Development, Enterprise AI Enablement.

WORK EXPERIENCE 

• SouthEast Colorado Hospital
• Data Scientist-AI/ML – Jan 2024 – Present 
• Co-developed and scaled HIPAA conscious application, a hospital-focused AI assistant for resolving IT issues in systems like Cerner and EHR, utilizing Hugging Face embeddings, OpenSearch vector search, and Retrieval-Augmented Generation (RAG) for accurate, goal-driven triage.
• Redesigned the application into a HIPAA-compliant, enterprise-grade solution, integrating Amazon Bedrock (Mistral LLMs), secure Lambda APIs, VPC-isolated OpenSearch, IAM-based access controls and KMS-encrypted data handling, ensuring safe, scalable healthcare deployment.
• Applied chain-of-thought reasoning and planning agents using LangChain and Python to automate IT ticket resolution workflows, contributing to a projected 70% reduction in L1 ticket volume.
• Designed a post-chatbot analytics pipeline that exports daily interaction logs from Amazon DynamoDB to Amazon Redshift using AWS Lambda and S3, enabling large-scale analysis of query patterns, fallback usage, and resolution efficiency.
• Built a HIPAA-complaint ETL pipeline to migrate, clean, and structure CHC Cerner IT support data for real-time AI integration using AWS S3, Redshift, and Lambda.
• Mentored junior engineers in GenAI workflows, prompt engineering, AWS best practices, and secure AI automation, creating internal documentation and hands-on walkthroughs.
• Implemented vector search and semantic matching to quickly identify similar candidate and role profiles, significantly improving sourcing speed and decision quality.
• Managed version-controlled deployments of models, prompts, embeddings, and data pipelines using AWS CodePipeline and S3, ensuring reliable and consistent releases across environments.
• Worked closely with business and IT stakeholders to gather requirements, align solutions with enterprise needs, and deliver clear, non-technical presentations to support decision-making and adoption.
• Mentored and trained junior team members, establishing documentation and knowledge-sharing practices to ensure continuity, scalability, and long-term operational success.

EDUCATION

Master of Science: Computer Science – May 2023
University of Massachusetts Lowell – Lowell, MA