AI Engineer with hands-on experience building LLM-powered applications, including RAG pipelines, conversational agents, and NLP systems. Skilled in Python, Hugging Face, and PyTorch, with a focus on developing practical, production-ready AI solutions. Strong interest in model optimization, scalable deployment, and real-world AI applications.
- Implemented AI-driven connectors to integrate Large Language Model services into enterprise applications and workflows.
- Developed Retrieval-Augmented Generation (RAG) pipelines to improve the accuracy and relevance of AI-generated responses.
- Configured prompts and guardrails to ensure AI interactions adhered to organizational safety and compliance standards.
- Translated architectural blueprints into functional software components using Java and JEE technologies within the SDLC.
- Maintained secure deployment of API orchestration layers to facilitate seamless data exchange between legacy systems and AI tools.
- Supported performance tuning initiatives to optimize cost and latency for enterprise-grade AI solution delivery.
- Built workflow automation patterns and event-driven architectures to support reliable diagnostic system integrations.
- Created observability scripts to monitor system performance and identify bottlenecks in AI service delivery.
- Developed backend logic using Python and Java to automate complex diagnostic workflows and data handling.
- Participated in the deployment of secure AI services and APIs in compliance with healthcare regulatory standards.
- Supported the implementation of batch processing modules for large-scale medical data ingestion and transformation.
- Contributed to technical documentation of system integration steps to ensure alignment with architectural designs.
- Automated data manipulation tasks using Java and Python to streamline the software development lifecycle for internal tools.
- Collaborated with senior engineers to debug AI application scripts and identify technical performance improvements.
- Implemented basic prompt engineering techniques to enhance the functionality of internal developer assistance tools.
- Updated technical documentation and maintained code libraries to support knowledge sharing across the development team.
- Participated in code reviews focused on responsible AI principles and secure software deployment practices.
- Completed programming assignments adhering to quality standards while learning functional programming and Inversion of Control (IoC) concepts.
• Developed an AI-driven image classification system using Python to automate oral cancer detection from medical imagery. • Implemented data preprocessing pipelines and API connectors to streamline the flow of medical data into analysis modules. • Configured system guardrails to maintain high accuracy and data privacy standards for sensitive medical diagnostics. • Updated performance tuning parameters to ensure low-latency processing for real-time diagnostic applications. • Collaborated on the integration of data access frameworks to manage large-scale medical image repositories. • Delivered detailed technical reports on AI model performance and validation metrics for cross-functional review.
• Built an automated data extraction engine utilizing Natural Language Processing (NLP) techniques to transform unstructured resumes into structured data. • Implemented logic inspired by Retrieval-Augmented Generation to match candidate profiles against specific job requirements for automated screening. • Created Python modules and REST APIs to facilitate the flow of data between frontend and extraction layers. • Tested parsing reliability across diverse document layouts to ensure consistent performance for enterprise usage. • Applied dependency injection to improve the modularity and maintainability of the parsing engine's backend components. • Designed visualization dashboards to monitor extraction accuracy and identify areas for model refinement.