SHWETA PAWAR
AI EngineerAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI Engineer with less than a year in Computer Vision & Generative AI
AI/ML Engineer with hands-on internship experience building production-grade computer vision pipelines and agentic AI systems. Proficient in LangChain, RAG architectures, PyTorch, and HuggingFace Transformers. Delivered video segmentation pipelines using Grounding DINO & SAM at ACL Digital, and optimized ML models via hyperparameter tuning at Ceryle Innovative Technologies. Passionate about building intelligent systems that solve real-world problems – from multi-agent workflows to real-time vision applications.
Centre for Development of Advanced Computing (CDAC)
Post Graduate Diploma in Artificial Intelligence · Artificial Intelligence
August 1, 2024 – June 30, 2024
University of Mumbai
Bachelor of Engineering · Computer Engineering
August 1, 2019 – June 30, 2023
Maharashtra State Board
HSC (Higher Secondary Certificate)
N/A – May 31, 2019
Ceryle Innovative Technologies
Machine Learning Intern
August 1, 2025 – November 1, 2025
Navi Mumbai, Maharashtra, India
ACL Digital
Data Science Intern
October 1, 2024 – April 1, 2025
Pune, Maharashtra, India
Agentic Mental Health Mapper
June 26, 2026 – Present
Built a multi-tool agentic AI system that maps community mental health indicators using NLP driven sentiment analysis on social and survey data, surfacing region-level risk scores for stakeholders. Designed LangChain agent pipelines with tool-calling capabilities, allowing the system to autonomously retrieve data, run sentiment models, and generate structured reports without manual intervention. Deployed an interactive Streamlit dashboard enabling users to filter by region, time window, and sentiment category - providing actionable mental health intelligence at a glance. Outcome: Demonstrated end-to-end agentic workflow with real data inputs, reusable tool architecture, and a production-style UI — directly applicable to LangChain/agentic AI roles.
Real-Time Sign Language Recognition System
June 26, 2026 – Present
Designed and deployed a real-time computer vision system that translates hand gestures (ASL alphabets & common words) into text using live webcam feed, achieving >90% recognition accuracy on test gestures. Implemented data augmentation (rotation, scaling, mirroring) to improve model generalization across varying hand sizes, lighting conditions, and camera angles. Optimized inference latency for real-time performance (<50ms per frame) by leveraging PyTorch model quantization and efficient OpenCV frame processing loops. Outcome: End-to-end vision project showcasing the full ML pipeline — data collection, model training, optimization, and live deployment — directly relevant to Computer Vision Engineer roles.
PDF Text-to-Audio Converter
June 26, 2026 – Present
Developed an API-based document accessibility tool that extracts text from multi-page PDFs and converts it to natural speech audio files using Pyttsx3's TTS engine. Outcome: Practical utility tool demonstrating backend API development, file processing, and deployment readiness - applicable to Python Developer and AI tooling roles.
Cultural Fit Analysis
The candidate's diverse project portfolio, including agentic AI, real-time computer vision, and utility tools, demonstrates a broad interest in AI applications and a willingness to tackle different types of challenges. Their internship experiences at Ceryle Innovative Technologies and ACL Digital show adaptability to different organizational contexts. The listed skills and project types align well with an AI Engineer role, indicating a strong cultural fit for a technically driven environment focused on innovation and practical application of AI.
Soft Skills & Operational Fit
The candidate's project descriptions highlight collaboration with cross-functional teams and documentation efforts, suggesting good team collaboration and communication skills. Their focus on real-world problem-solving indicates a practical and results-oriented work attitude. The detailed project outcomes demonstrate a structured approach to problem-solving and a commitment to delivering functional solutions.