IT Consultant
About Me
Senior Data Engineer and ML Specialist with over 6 years of experience architecting end-to-end AI systems and high-scale data pipelines. Expert in transforming complex product requirements into production-ready LLM and ML solutions, specializing in Retrieval-Augmented Generation (RAG), agentic workflows, and LLMOps. I bridge the gap between raw data and actionable intelligence within highly regulated sectors like Banking and Healthcare.
Value Proposition: Delivering scalable, secure AI infrastructure that optimizes decision-making and operational efficiency.
Measurable Impact: Engineered pipelines processing multi-terabyte datasets and deployed LLM frameworks reducing manual reporting cycles by 40%.
Core Expertise: AI System Design, Distributed Data Processing (Spark/Databricks), LLM Prompt Engineering, and Cloud Data Warehousing.
Technologies: Python, SQL, AWS, Databricks, Spark, LangChain, PyTorch, Snowflake, Docker, Kubernetes.
Skills
AI/ML: LLM, NLP, RAG, Prompt Engineering, LangChain, PyTorch, Transformers, LLMOps, Agentic Workflows.
Data Engineering: Python, SQL, PySpark, Databricks, Spark, Hadoop, Hive, Kafka, ETL/ELT, dbt, Alteryx.
Cloud & DevOps: AWS, Snowflake, Redshift, BigQuery, Docker, Kubernetes, Airflow, Oozie, Sqoop.
Analytics: Power BI, DAX, Power Query, Statistical Data Analysis, Metadata Modeling.
Domain: Banking, Healthcare, HIPAA Compliance, FinTech, NCQA Standards.
Skills
Project ManagementProblem SolvingPythonSQLAWSKubernetesAzureReportingStrategic PlanningGCPNetworkingTensorFlow
Education
Bachelor of Science in Computer Science
Master of Science in Data Science
GPA: 3.70
Experience
Architected scalable IT strategies using Python and SQL to automate financial lead scoring, resulting in a 40% increase in conversion rates for high-volume financial analytics. Advised on network solutions and cloud infrastructure using AWS and Spark to ensure 100% data integrity and consistency across automated financial service workflows. Spearheaded deployment of secure IT systems leveraging TensorFlow and AWS SageMaker across three large-scale production pipelines, driving a 35% reduction in risk analysis false positives. Managed technical configuration of Databricks and AWS Redshift over 12 months, processing 1TB+ weekly to enable real-time analytics and exceed uptime KPIs at 99.98%. Implemented system security and HIPAA compliance protocols for financial data platforms using enterprise encryption, ensuring 100% adherence to industry standards and zero data breaches. Collaborated with internal teams to define IT project goals and deliverables, utilizing Microsoft Office Suite to maintain 100% on-time project completion across five initiatives.
Developed scalable data pipelines for healthcare analytics using Python and OpenAI API, accelerating customer response times by 35% through enhanced data reliability and HIPAA compliance. Integrated TensorFlow and Azure ML pipelines to deliver a unified IT analytics platform, reducing feature delivery timelines by 30% through proactive technical follow-through with cross-functional teams. Analyzed IT environments to identify optimization opportunities leveraging Kubernetes to support 5+ concurrent healthcare projects, driving a 20% uplift in predictive model accuracy. Engineered robust IT systems for real-time data analysis utilizing Apache Spark and Python to handle 2TB+ daily data volume while supporting compliance with enterprise security SLAs. Monitored distributed ETL workflows using Apache Airflow and Great Expectations to improve data quality, reducing data inconsistencies by 35% and ensuring system integrity for enterprise-grade healthcare initiatives. Limited viewing of PHI to absolute minimums by implementing specialized access controls, achieving 100% compliance with Information Security and HIPAA policies.
Facilitated large-format workshops and hands-on enablement sessions for graduate engineering students, increasing student proficiency in AI coding tools and SDLC patterns by 35%. Authored technical guides and reference implementations for the university’s data engineering curriculum, prioritizing clarity and pedagogy for complex technical concepts. Evaluated and prototyped novel AI-driven solutions hand-in-hand with research teams, contributing high-signal demos for departmental AI deployment initiatives.
Deployed production-ready IT systems for call automation using Python and SQL, achieving a 20% reduction in customer acquisition costs for enterprise-grade platform engineering. Integrated distributed network solutions for lead management on modern data platforms, improving processing efficiency and data consistency by 30%. Managed end-to-end deployment of IT-enabled functionality on cloud infrastructure using Microsoft Office Suite for reporting, scaling support for 15+ user teams and boosting retention by 28%. Advised on software and hardware solutions to enhance operational efficiency across diverse teams, resulting in a 15% reduction in system latency and improved scalability. Ensured system security and data integrity by implementing robust validation protocols, maintaining 99.9% data accuracy across all integrated systems and applications.