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Generative AI Engineer with less than a year in LLMs, Python, and machine learning pipelines.
AI is analyzing your overall score…
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Motivated AI/ML Engineer with hands-on experience designing and deploying LLM-powered applications, machine learning pipelines, and intelligent automation workflows. Proficient in Python, deep learning frameworks, Generative AI APIs, and workflow automation tools including n8n. Adept at translating complex technical requirements into scalable, production-ready AI solutions. Seeking an entry-level AI/ML or Generative AI Engineer role to build intelligent systems that deliver measurable business impact.
Kakatiya Institute of Technology and Sciences
B.Tech · Artificial Intelligence & Machine Learning (AIML)
N/A – June 30, 2026
AI Code Review Tool
June 15, 2026 – Present
Identified a gap in automated code quality tooling; engineered an LLM-powered system to analyze Python and Java codebases for bugs, security vulnerabilities, and performance bottlenecks. Designed a multi-stage prompt engineering pipeline that generates refactored code suggestions, reducing manual code review effort by up to 60%. Implemented rule-based post-processing validation to ensure suggestions adhere to coding standards, improving output reliability and developer trust.
AI-Powered IT Ticket Resolution Assistant
June 15, 2026 – Present
Addressed high-volume IT support bottleneck by architecting an automated ticket solver leveraging Qwen3-Next-80B and Mistral-7B models via HuggingFace Inference API. Built a FastAPI backend with Pydantic schema validation to parse complex system logs and generate structured 4-step actionable resolution plans, cutting average resolution time by ~40%. Deployed RESTful API endpoints to integrate the assistant into existing IT workflows, enabling seamless ticket triaging and escalation routing.
AI-Based Stock Market Analysis System
June 15, 2026 – Present
Engineered an end-to-end ML pipeline to analyze 5+ years of historical market data, applying feature engineering and statistical modeling to surface actionable investment trends. Implemented and benchmarked multiple predictive models (regression and classification), achieving data-driven forecasting accuracy to support investment decision workflows. Automated data ingestion and preprocessing routines, reducing data preparation time by 50% and enabling near-real-time model updates.
Complete hands-on training in LLM application development, RAG pipelines, and GenAI tooling (HuggingFace, OpenAI API).
Unknown
June 1, 2026 – Present
Practical experience with n8n automation platform for building AI-driven multi-step workflow pipelines.
Unknown
June 1, 2026 – Present
Self-directed study in advanced prompt engineering techniques, chain-of-thought reasoning, and structured output generation.
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest and initiative in applying AI/ML to solve real-world problems, aligning well with an innovative and problem-solving culture. The diversity of projects (IT support, code review, financial analysis) shows a broad application interest. The self-directed learning and certification efforts indicate a proactive and continuous learning mindset, which is valuable for cultural fit in a rapidly evolving field like Generative AI. However, all projects are personal, so direct experience in collaborative team environments is not evident.
Soft Skills & Operational Fit
The candidate's resume highlights 'Analytical Thinking', 'Problem-Solving', 'Team Collaboration', and 'Adaptability' as soft skills. Project descriptions suggest problem-solving (e.g., addressing IT support bottlenecks, identifying gaps in code quality tooling) and an analytical approach to system design. The focus on building production-ready solutions indicates an operational mindset, though direct experience in a team or corporate setting is not provided.