Adil – Python, AI, Machine learning

At a leading sports technology platform, developed a real-time computer vision system to track players and equipment on the field, providing automated insights for performance analysis through deep learning models detecting movements, positions, and events.

• Developed sports analytics pipeline.
• Developed activity recognition model from scratch.

Architected an AI Legal Agent for a legal tech platform, leveraging LLMs, RAG, and intelligent document processing to ingest complex legal documents like contracts, NDAs, and regulatory filings—extracting key clauses, summarizing obligations, and providing accurate, cited answers to nuanced legal questions.

• Led design and end-to-end development of AI agent automating legal workflows.
• Built clause extraction, obligation summarization, and grounded Q&A with source citations.
• Implemented RAG pipeline including parsing, chunking, embedding for precise context retrieval.
• Created intelligent document processing to reduce manual review and boost decision confidence.

A defense-related automation initiative where Adil designed and deployed a secure, event-driven system to streamline multi-source data ingestion, validation, and decision support workflows. He built a modular pipeline that automatically processed structured and unstructured inputs, applied rule-based and model-assisted validation, and routed outputs through a series of orchestrated agents for enrichment and prioritization. The system reduced manual review overhead by introducing confidence scoring, exception handling, and human-in-the-loop checkpoints only when necessary. Adil also implemented audit logging, traceability, and role-based access controls to meet strict compliance requirements, while optimizing for low latency and high reliability in a constrained environment.

• Reduced manual processing time by over 60 percent through end to end automation of ingestion validation and routing workflows.
• Improved data accuracy and consistency by implementing multi layer validation and confidence scoring with human in the loop only for edge cases.
• Increased system throughput by enabling parallel agent orchestration and event driven processing.
• Strengthened compliance and audit readiness with full traceability logging and role based access controls.
• Decreased operational bottlenecks by automating exception handling and prioritization of critical cases.

Collaborated with a healthcare analytics company to clean and ingest complex electronic medical records from multiple provider networks using diverse systems and formats. Built a Python/SQL pipeline to parse, validate, normalize data — handling inconsistent schemas, missing values, duplicates, and PHI removal via regex/tokenization for HIPAA compliance. Delivered production-ready data warehouse powering clinical dashboards and predictive models, working closely with analysts and compliance teams for integrity and audit readiness.

• Analyzed sensitive client healthcare data from multiple sources.
• Ingested complex electronic medical records across diverse systems and formats.
• Standardized diagnosis codes, medications, encounter types, and ensured HIPAA-compliant PHI removal.