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Generative AI Engineer with less than a year in Data Science, Prompt Engineering, and RAG Pipelines.
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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
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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
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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.