# Adit Shah > AI/ML Engineer & Lead Software Developer @ Omnic.AI | 3+YOE | MIT Sloan Analytics Challenge Finalist | MS in Computer Science @ Northeastern University | Open to Full-Time Opportunities Starting July 2025 Location: Boston, Massachusetts, United States Profile: https://flows.cv/adit AI/ML Engineer with 4+ years of experience designing, deploying, and monitoring machine learning systems across computer vision and NLP domains. Experienced in building scalable data pipelines, implementing transformer-based and RAG architectures, and deploying containerized models on AWS. Strong background in model optimization, experiment tracking, and production lifecycle management, with a focus on measurable performance improvement and system reliability. ## Work Experience ### AI/ML Engineer @ Omnic.AI Jan 2024 – Present | Brunswick, ME • Designed and deployed NLP-driven automation workflows for enterprise document processing, improving information extraction precision by 15% across client-facing analytical use cases. • Built Retrieval-Augmented Generation pipelines integrating transformer-based embeddings with vector indexing, increasing contextual response relevance by 18% in internal evaluation benchmarks. • Developed scalable preprocessing and feature engineering workflows using Python and SQL, reducing data preparation turnaround time by 24% across iterative model development cycles. • Containerized model services using Docker and deployed REST-based inference endpoints, maintaining average response latency under 320ms during controlled load testing. • Implemented structured experiment tracking and version management practices, improving reproducibility and shortening experimentation cycles by approximately 20%. • Established monitoring procedures to track prediction drift and retrieval performance, enabling timely retraining and reducing degraded outputs by 11% over quarterly review periods. ### Research Assistant (ML / Data Engineering) @ Northeastern University Jan 2024 – Jan 2024 • Developed a CNN-LSTM architecture to map single-cell sequencing mRNA data to surface protein markers, achieving 78% validation accuracy for early-stage cancer biomarker prediction. • Engineered preprocessing workflows incorporating Short-Time Fourier Transform (STFT) signal transformations, improving feature extraction consistency across high-dimensional biomedical datasets. • Built automated data ingestion and transformation pipelines to standardize sequencing inputs, reducing manual preprocessing effort by approximately 27%. • Conducted structured model evaluation using cross-validation and confusion matrix analysis to assess predictive stability across multiple biological cohorts. • Collaborated with cross-disciplinary research teams to translate experimental hypotheses into deployable machine learning workflows aligned with laboratory objectives. • Documented reproducible training procedures and environment configurations, supporting consistent experimentation and reducing setup inconsistencies during research iterations. ### AI/ML Engineer @ Capgemini Jan 2021 – Jan 2023 • Built supervised learning models for forecasting and classification use cases within financial services engagements, improving prediction reliability by 11% compared to legacy rule-based decision systems. • Engineered feature pipelines handling structured and semi-structured datasets exceeding 3 million records, improving data readiness timelines by 25% and minimizing manual preprocessing dependencies. • Implemented computer vision solutions leveraging CNN architectures and optimized inference using TensorRT, reducing processing time per image by 18% within production reporting systems. • Deployed REST-based model services integrated into client applications, ensuring stable API performance with average response times under 350ms during high-volume transaction periods. • Collaborated with cross-functional delivery teams across analytics and engineering units, aligning model outputs with business KPIs and contributing to a measurable 9% operational efficiency gain. • Supported post-deployment evaluation processes including error analysis and periodic retraining cycles, reducing performance variance by 10% across quarterly assessment benchmarks. ## Education ### Master's degree in Computer Science Northeastern University ### B TECH in Computer Science SVIT, Vasad Official ## Contact & Social - LinkedIn: https://linkedin.com/in/adit-shah9 --- Source: https://flows.cv/adit JSON Resume: https://flows.cv/adit/resume.json Last updated: 2026-03-28