Spurthi Pattanashetti
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 1+ years in AI Systems & Generative AI applications.
AI Engineer and Electronics & Communication graduate with experience building production-ready AI systems, intelligent agents, and Generative AI applications. Currently working as a Forward Deployed Engineer Intern, contributing to AI-driven products and workflow automation in real-world business environments. Skilled in designing end-to-end AI solutions involving RAG, agentic workflows, semantic search, intelligent automation, and LLM-powered applications. Experienced in integrating AI models into scalable products and deploying solutions from prototype to production. Passionate about building practical AI systems that solve business problems and enhance user experiences.
DataMites Institute
AI Engineering Certification
N/A – Present
KLE Technological University
B.E. · Electronics & Communication Engineering
N/A – June 30, 2025
Forward Deployed Engineer Intern
Forward Deployed Engineer Intern
March 1, 2026 – Present
India
Rubixe AI
Data Science Consultant
July 1, 2025 – February 1, 2026
India
AutoretroTech Pvt Ltd
AI / IoT Developer Intern
February 1, 2025 – May 1, 2025
India
AI Smart Product Recommender
June 24, 2026 – Present
Built a multimodal product search pipeline using CLIP for joint image-text embedding and similarity matching between user-uploaded images and product catalog categories. Designed a hybrid recommendation engine combining semantic vector search, cosine similarity ranking, and rule-based filtering for relevant product suggestions. Implemented text-based semantic retrieval using Sentence Transformers embeddings for intelligent product category matching from natural language queries. Integrated Gemini API for conversational product discovery with fallback mechanisms ensuring high system reliability and response consistency.
View ProjectProduction AI - ML Pipeline, FastAPI & RAG
June 24, 2026 – Present
Built a machine failure prediction model on 100,000 sensor readings (0.5% failure rate) using XGBoost and Random Forest with imbalanced-learn; evaluated via PR-AUC and F2-score with cost-based threshold tuning to minimize missed failures. Engineered a Python log processor for 50GB files using generator-based streaming, reducing space complexity from O(N) to O(1); exposed via an async FastAPI endpoint with structured JSON responses and robust error handling. Built an HR Policy RAG chatbot with section-aware document chunking, FAISS vector retrieval, and confidence guardrails ensuring reliable, grounded responses over policy documents. Packaged all services into Docker containers orchestrated with docker-compose for consistent, reproducible deployments across environments.
View ProjectAI Business Audit System
June 24, 2026 – Present
Built a full-stack AI-powered lead automation system using Next.js and Google Gemini API to generate personalized business audit reports from website inputs. Automated workflows using Playwright for website scraping, Puppeteer for PDF report generation, and Resend for email delivery — end-to-end with no manual steps. Integrated Google Sheets API for real-time lead logging with input validation, structured error handling, and full API response management. Designed a complete workflow orchestration pipeline covering lead intake, AI enrichment, PDF generation, email dispatch, and reporting in a single automated flow.
View ProjectAI Document Q&A Assistant
June 24, 2026 – Present
Built a Retrieval-Augmented Generation system enabling natural language querying over PDF documents using Gemini-style LLM with FAISS-backed semantic search. Designed an optimized chunking and retrieval pipeline balancing context window efficiency with retrieval precision for accurate, grounded document responses. Integrated LLM API to generate context-aware responses and improved output quality through prompt tuning and retrieval optimization. Developed a real-time Streamlit interface for users to upload documents and query them interactively with instant, context-aware answers.
View ProjectCultural Fit Analysis
The candidate's diverse project portfolio, ranging from AI-powered recommenders to RAG chatbots and IoT integrations, demonstrates adaptability and a broad interest in applying AI across different problem spaces. Their experience with both personal projects and internships indicates initiative and a proactive learning approach, aligning well with a dynamic AI engineering environment. The mention of optimizing for recall in critical systems (ITSM ticket prediction) and cost-based threshold tuning shows a practical, impact-driven mindset.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong problem-solving aptitude and a focus on building practical, end-to-end solutions. Their experience with workflow automation and system reliability suggests an operational mindset. The detailed descriptions of project challenges and solutions imply good communication of technical concepts.