Generative AI Engineer with less than a year in Python, LLMs, and machine learning pipelines, seekin
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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)
August 1, 2022 – June 30, 2026
AI Code Review Tool
January 1, 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
January 1, 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
January 1, 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 proactive approach to identifying and solving real-world problems using AI, which aligns with an innovative and impact-driven culture. The academic nature of all projects, combined with an expected graduation date in 2026, suggests a strong learning orientation. However, the lack of professional experience or diverse team projects makes it difficult to fully assess cultural fit beyond a theoretical alignment with problem-solving and learning.
Soft Skills & Operational Fit
The candidate's resume highlights analytical thinking, problem-solving, team collaboration, and adaptability as soft skills. These are crucial for a Generative AI Engineer role, especially in a dynamic environment. The project descriptions indicate an ability to identify problems and engineer solutions, suggesting good operational fit for a role requiring initiative and practical application of AI.