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AI Engineer with less than a year in Generative AI & Prompt Engineering
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Highly motivated and results-driven individual with hands-on experience in Generative AI and prompt engineering, complemented by a strong academic background in Computer Science and Engineering with a specialization in Artificial Intelligence & Machine Learning. Proven ability to design and implement scalable AI-driven solutions, develop responsive web applications, and optimize system performance. Eager to contribute expertise in RAG workflows, LLM-based systems, and full-stack development to innovative projects.
Manipal University Jaipur
B.Tech · Computer Science and Engineering (Artificial Intelligence & Machine Learning)
August 1, 2023 – June 30, 2027
PwC (PricewaterhouseCoopers)
Launchpad Salesforce Trainee
February 1, 2026 – Present
India
TechLearn Solutions
Frontend Developer (Summer Internship)
June 1, 2025 – July 1, 2025
India
DocQueryAI
June 1, 2026 – Present
Built an end-to-end RAG pipeline using HuggingFace embeddings and FAISS vector store to enable semantic document querying and context-grounded answer generation via Groq (LLaMA-3). Improved retrieval precision by 30-40% using cosine similarity, adaptive score-gap filtering, and refined chunking with overlap, reducing irrelevant context in top-k results.
View ProjectAskQL
June 1, 2026 – Present
Developed an LLM-powered Text-to-SQL system achieving 80-90% accuracy using schema-conditioned prompting and structured output constraints. Architected an end-to-end NL-to-SQL pipeline (user query → LLM → SQL generation → database execution → response rendering), reducing invalid queries by 30% and ensuring reliable database interactions.
View ProjectAgentic Blueprint AI
June 1, 2026 – Present
Built an end-to-end AI exam paper generator that extracts structured blueprints (sections, marks, patterns) from PDFs using pdfplumber + regex, achieving 95% parsing accuracy across varied formats and reducing manual effort by 80%. Engineered a controlled LLM pipeline with LLAMA 3 (Groq API) for context-aware question generation, improving output consistency by 40% via prompt engineering and schema parsing, while reducing latency by 3x using parallel execution (ThreadPoolExecutor).
View ProjectCAT AI Interviewer
June 1, 2026 – Present
Engineered an agentic interview orchestration workflow using LangGraph and Groq LLM, enabling contextual memory, dynamic topic switching, and multi-turn follow-up questioning across projects, internships, leadership experiences, and career goals. Developed an AI evaluation pipeline generating 8 assessment metrics including communication, leadership, problem-solving, technical skills, and MBA readiness, while integrating real-time Voice AI through Kokoro TTS and Speech Recognition, reducing manual interview assessment effort by 80%+.
View ProjectRed Hat RHCSA I & II
Red Hat
June 1, 2026 – Present
Python Essentials 1 & 2 (Cisco)
Cisco
June 1, 2026 – Present
NPTEL Machine Learning
NPTEL
June 1, 2026 – Present
NPTEL Design and Analysis of Algorithms
NPTEL
June 1, 2026 – Present
Oracle Database Certification
Oracle
June 1, 2026 – Present
Agile Certification
Unknown
June 1, 2026 – Present
NPTEL Elite Silver Certifications (2×)
NPTEL
June 1, 2026 – Present
Cultural Fit Analysis
The candidate demonstrates a strong cultural fit for an AI Engineer role through a diverse portfolio of personal AI projects, active participation in ideathons, and relevant certifications. The projects showcase a passion for AI and problem-solving, aligning well with an innovative and fast-paced technical environment. The breadth of skills across AI/ML, programming languages, and databases indicates adaptability and a continuous learning mindset.
Soft Skills & Operational Fit
The candidate's project descriptions highlight problem-solving, attention to detail (e.g., 95% parsing accuracy, 30-40% retrieval precision improvement), and an ability to work on complex, multi-faceted AI systems. The internship at PwC suggests an understanding of enterprise-level solutions and cloud architecture. The diverse project portfolio indicates initiative and a proactive approach to learning and applying new technologies.