Generative AI Engineer with less than a year in Data Science, Prompt Engineering, and RAG Pipelines.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Computer Science (Data Science) graduate with hands-on internship experience building generative AI applications, automation pipelines, and analytics dashboards. Comfortable working across the AI development lifecycle, from data preparation and prompt design to API integration and deployment, with a steady focus on turning ideas into working, testable systems. Known for asking the right questions during requirements discussions and presenting findings in a way both technical and non-technical stakeholders can act on. Looking to bring this practical, detail-oriented approach to a Generative AI Engineering team where I can keep learning fast, take ownership of real problems, and contribute from day one.
Atria Institute of Technology, VTU
B.E. · Computer Science (Data Science)
August 1, 2022 – June 30, 2026
KodNest
Data Science Intern
March 1, 2026 – Present
India
Learner's Byte
Generative AI Intern
February 1, 2026 – April 1, 2026
India
Roman Technology
AI Agent Developer Intern
January 1, 2026 – April 1, 2026
Bengaluru, Karnataka, India
OdoShield - Used Car Fraud Auditor
January 1, 2026 – June 13, 2026
Built during a HackerRank-orchestrated hackathon under time pressure, I worked with my team to design a 6-layer odometer fraud detection system spanning registry timeline analysis, ECU module cross-checks, an XGBoost fraud-probability model, a PyTorch CNN for wear assessment, and a Hugging Face LLM forensic report – aggregated into one weighted fraud-risk score and deployed with a FastAPI backend and Node/Express frontend.
View ProjectFinancial Data Analyst Agent
January 1, 2026 – June 13, 2026
To address how long manual financial reporting was taking a typical team, I designed and built a seven-agent pipeline (ingestion → cleaning → feature engineering → EDA → visualization → report generation → RAG chatbot), taking it from prototype through to deployment. I paired this with Power BI/Looker Studio-compatible dashboards and a context-aware RAG chatbot for natural-language querying, which brought financial reporting time down from around 4 hours to under 10 minutes; deployed live on Google Cloud Run.
AI Job Agent Platform
January 1, 2026 – Present
Aiming to make job searching less manual and more targeted, I am architecting a multi-agent automation platform with structured SQL database management and relevance-based ranking, applying supervised/unsupervised learning concepts and dashboards to surface the most relevant opportunities at scale.
View ProjectMicrosoft Azure Data Scientist Associate (DP-100)
Microsoft
June 13, 2026 – Present
IBM Data Science & Data Analyst Professional
IBM
June 13, 2026 – Present
Databricks Mosaic AI Practitioner
Databricks
June 13, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, including professional and academic projects, showcases a broad interest and adaptability. Their experience in multiple internship roles (Data Science, Generative AI, AI Agent Developer) indicates a willingness to explore different facets of AI and data science, suggesting a good fit for a dynamic, learning-oriented culture. The focus on practical, real-world problem-solving and collaboration aligns with a results-driven team environment. The candidate's stated desire to 'grow alongside an experienced team, contribute to real production systems, and take on responsibility quickly' indicates a strong cultural alignment with a growth-oriented and impactful role.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills by identifying inefficiencies and developing automated solutions. Their ability to collaborate with cross-functional teams and translate business requirements into technical solutions is evident. The candidate also shows a proactive attitude towards learning and improving model performance through prompt engineering research. The professional summary highlights a detail-oriented approach and a focus on turning ideas into working, testable systems, which aligns well with operational rigor.