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AI Engineer with 1+ years in Computer Vision & LLM-based systems
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
As a Jr. AI/ML Engineer, I specialize in AI-driven backend and computer vision projects across clinical research, industrial safety, and business automation. I have developed FastAPI-based backend APIs for AI/ML workflows and implemented computer vision solutions using YOLO, OpenCV, and object tracking for various detection tasks. My expertise extends to integrating LLM and RAG-based systems with Gemini AI, Qwen3, and Fireworks AI, and managing data with PostgreSQL, MongoDB, Redis, and Vector Databases. I am proficient in API testing, deployment, and requirement analysis.
Prestige Institute of Engineering Management and Research
Bachelors of Technology · Computer Science
August 1, 2020 – June 30, 2024
India International School
Higher Secondary
June 1, 2018 – May 31, 2019
India International School
Secondary Education
June 1, 2016 – May 31, 2017
Moreyeahs IT Technologies
Jr. AI/ML Engineer
May 1, 2025 – Present
Indore, Madhya Pradesh, India
AI-Powered Video Analytics & Detection Platform
June 27, 2026 – Present
Worked on an AI-powered video analytics platform for real-time monitoring of industrial and commercial CCTV/live camera feeds. Developed and integrated custom YOLO-based detection models for use cases such as PPE detection, fire/smoke detection, object tracking, and movement monitoring. Built backend workflows to process live RTSP streams and uploaded videos for automated safety and operations monitoring. Implemented configurable ML templates with detection thresholds, schedules, camera-wise rules, and department-based alert notifications. Supported features such as line crossing, height-line tracking, path history, movement tracking, acknowledgement tracking, and audit logs. Developed APIs for alert workflows, detection results, reporting dashboards, and searchable incident records. Helped reduce dependency on manual CCTV monitoring by enabling real-time alerts and compliance-focused reporting.
Defect Detection & Image Preprocessing System for Anode Manufacturing
June 27, 2026 – Present
Developed a real-time computer vision-based defect detection system for anode manufacturing to identify and classify industrial defects during production. Trained and tested custom YOLOv8 and Detectron2 models for defect classification, localization, and quality inspection. Built an image preprocessing pipeline using OpenCV and NumPy to enhance input image quality for micro-defect detection in high-heat industrial environments. Applied preprocessing techniques such as grayscale conversion, contrast enhancement, noise reduction, thresholding, ROI cropping, and augmentation to improve model performance. Used Tesseract OCR to extract batch and production metadata from images for traceability and reporting. Developed a modular FastAPI backend for on-demand image transformation, model inference, and integration with factory systems through REST APIs.
AI-Powered Clinical Trial Data Processing & SDV Automation Platform
June 27, 2026 – Present
Worked on an AI-powered clinical trial platform for processing clinical documents and EDC/ODM data to support automated Source Data Verification (SDV). Developed backend workflows for clinical document upload, document registry creation, OCR processing, and metadata/context extraction. Contributed to ODM XML ingestion by parsing clinical trial data and storing raw records into staging tables for further transformation. Assisted in building AI-assisted CDIM mapping workflows, where source EDC fields are mapped to standardized clinical data domains. Worked on transformation logic to move approved mapped data from staging tables into clinical domain tables such as AE, LB, CM, DM, and related CDIM structures. Supported SDV comparison logic to validate source document data against EDC/CDIM data and generate statuses such as MATCH, MISMATCH, MISSING INSOURCE, MISSINGINEDC, and NO DATA. Integrated asynchronous processing using Celery and Redis for OCR, document processing, classification, and extraction tasks. Built and tested REST APIs for study management, document processing, mapping approval, transformation, and SDV result tracking. Assisted in deployment and UAT support using Uvicorn, PostgreSQL, and environment-based backend configuration. Created a batch execution framework for plug-and-play integration with multiple ML models. Containerized the application using Docker for consistent deployment and execution.
AI-Powered Clinical Trial Site Feasibility Platform
June 27, 2026 – Present
Developed backend modules for an AI-powered clinical trial feasibility and site selection platform to help identify and rank suitable clinical trial sites. Built workflows for site search, filtering, scoring, and ranking based on study requirements such as therapeutic area, study phase, location, recruitment capability, and quality metrics. Worked on Excel-based clinical site data processing to fetch and evaluate matching site records through API-driven workflows. Implemented selected-site management to store site details, rankings, scores, investigator information, user details, and job-level tracking. Developed feasibility questionnaire workflows to send site questionnaires, collect responses, and handle document uploads from clinical sites. Integrated Google Gemini AI for summarizing uploaded site documents and extracting important feasibility-related insights. Worked on protocol document extraction for PDF/DOCX files to identify key sections such as study overview, inclusion/exclusion criteria, study design, safety requirements, site responsibilities, and regulatory details. Supported secure backend features including JWT authentication, OTP verification, password reset, user invitations, superadmin access, user activation/deactivation, and audit logs. Built and tested REST APIs for authentication, site feasibility search, scoring, questionnaire handling, document upload, AI summarization, and protocol extraction.
Enterprise AI Worker Management Platform
June 27, 2026 – Present
Contributed to an enterprise AI automation platform for creating and managing customizable AI workers across business functions. Worked on AI worker configuration, knowledge file upload workflows, internal chat testing, and partner-level management features. Supported authentication flows including login, OTP verification, password setup, and secure partner onboarding. Assisted in building scalable AI worker workflows with web search support and WhatsApp integration for business communication.
Cultural Fit Analysis
The candidate's experience spans multiple domains including industrial safety, clinical research, and general business automation, showcasing versatility and a broad interest in applying AI solutions. This diversity of projects, coupled with the use of a wide array of modern AI/ML and backend technologies, suggests a proactive and adaptable individual who would likely integrate well into a dynamic and innovative team culture. The focus on real-world problem-solving aligns with a results-oriented environment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong ability to work on complex, multi-faceted AI projects, suggesting good problem-solving skills and a structured approach to development. The collaboration on API testing, debugging, deployment support, requirement analysis, and UAT issue resolution points to a team-oriented mindset and operational awareness. The diverse project portfolio also suggests adaptability and a willingness to tackle varied technical challenges.