Help & Supportcareers@gravityer.com
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 Generative AI & RAG Pipelines
AI & ML Engineer with hands-on experience in Python, machine learning, LangChain, RAG pipelines, and Agentic AI workflows. Proficient in building data-driven solutions using Scikit-learn, TensorFlow, PyTorch, and CNN-based architectures. Familiar with LLM concepts, prompt engineering, vector search, and document ingestion pipelines. Experienced with Microsoft Autogen for multi-agent orchestration. Eager to design and deploy production-grade Generative AI applications on cloud platforms using LangChain, LangGraph, and modern microservices patterns.
JNTUA College of Engineering, Pulivendula
B.Tech · Computer Science and Engineering
August 1, 2021 – June 30, 2025
Arka Mediaworks
Data Science Intern (Virtual)
November 1, 2025 – January 31, 2026
Bengaluru, Karnataka, India
Image Recognition System for Medical Diagnosis
June 28, 2026 – Present
Developed a CNN-based AI image classification system to support medical diagnosis by categorizing medical images into clinically relevant classes. Applied preprocessing techniques — resizing, normalization, and data augmentation — to improve model robustness and generalization across unseen data. Evaluated model performance using accuracy, precision, recall, F1-score, and confusion matrix; conducted hyperparameter tuning to optimize prediction quality.
Content-Based Movie Recommendation System
June 28, 2026 – Present
Built a machine learning recommendation engine using TF-IDF representations and cosine similarity to generate top-N movie recommendations from structured metadata. Implemented text preprocessing and semantic feature engineering to improve matching relevance, producing reusable recommendation logic ready for integration into AI applications. Structured the solution as a modular pipeline — analogous to a document ingestion and retrieval workflow — enabling easy extension to RAG-based architectures.
Agentic AI Workflow – Multi-Agent Document Q&A System
June 28, 2026 – Present
Conceptualized and prototyped an agentic AI workflow using LangChain and Microsoft Autogen to orchestrate multi-agent pipelines for document ingestion, embedding, and response generation. Designed a RAG-based knowledge retrieval pipeline incorporating document chunking, vector embedding, and semantic search to deliver contextually accurate LLM responses. Experimented with agent orchestration patterns including task delegation, tool-use, and context passing between agents using Autogen's multi-agent framework.
Advanced Certification in Data Science & AI/ML
upGrad
June 1, 2026 – Present
Content-Based Movie Recommendation System
Arka Mediaworks
June 1, 2026 – Present
Yuva AI for All
TCS iON
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest and hands-on experience in cutting-edge AI domains like Generative AI, RAG, and multi-agent systems, which aligns well with an AI Engineer role. The diversity of projects (Q&A, medical imaging, recommendations) shows a broad application interest. The self-driven nature of personal projects and certifications indicates a proactive learning attitude. The conceptual understanding of cloud and DevOps tools suggests an awareness of broader system integration, which is positive for cultural fit in a modern engineering team.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to conceptualize, design, and prototype complex AI systems, suggesting strong problem-solving and analytical skills. The modular pipeline design approach in projects points to an understanding of maintainability and scalability. The virtual internship experience suggests adaptability and self-management. However, without direct interview data, specific soft skills like teamwork, leadership, or conflict resolution cannot be fully assessed.