SHREYA BHAT

GENAI ENGINEER

Building reliable LLM systems, RAG pipelines and agentic AI applications.

Hands-on experience building LLM-powered pipelines, agentic workflows, and multimodal document-processing systems β€” with a focus on RAG, evaluation (RAGAS), and applying GenAI to real operational problems.

Pixel-art adventure landscape representing a journey from curiosity to AI solutions
ABOUT

ABOUT ME

My background sits at the intersection of hardware and software: a B.E. in Electronics & Instrumentation gave me a grounding in the sensors, control systems and industrial data (like SCADA/BMS) that pharmaceutical manufacturing runs on. Working as a Trainee in Manufacturing & Formulation at CIPLA Ltd put me close to that operational data day-to-day, which is what pulled me toward applying GenAI and machine learning to real industrial problems β€” from anomaly detection to RAG-based knowledge retrieval. I'm now focused on building reliable LLM systems, RAG pipelines and agentic AI applications, informed by that hands-on instrumentation and manufacturing background.

INDUSTRIAL AI EXPERIENCE

INDUSTRIAL AI WORK @ CIPLA LTD

Applying GenAI, retrieval, and anomaly detection to real plant operations.

Trainee – Manufacturing & Formulation Β· July 2024 – June 2025
Internal Industrial Deployment

My official role was in Manufacturing & Formulation. The GenAI/RAG and anomaly-detection work below was developed as part of that role, supporting plant operations.

WHAT I BUILT

πŸ“š Knowledge Retrieval for Equipment Troubleshooting

Built a RAG-based document processing system using LangChain and pgvector to index and retrieve information from 1,000+ equipment logs and SOPs.

Impact: Monthly troubleshooting job cards dropped from 120+ to ~40 β€” approximately a 67% reduction.

🚨 Sensor Anomaly Detection + Root-Cause Analysis

Worked with time-series data from 1,400+ sensors across 15 SCADA and 47 BMS systems to identify abnormal equipment behaviour. Connected anomaly findings with the knowledge retrieval layer to support root-cause investigation using relevant SOPs and historical records.

Impact: Root-cause analysis time reduced by 55–60%.

πŸ§ͺ LLM Evaluation & Reliability

Evaluated RAG outputs against a 100-observation golden dataset using RAGAS metrics including faithfulness, context recall, and answer relevancy. Used evaluation results to identify weaknesses in retrieval and response quality and iterate on the LLM workflows.

RECOGNITION

πŸ† Recognized as β€œPerformer of the Month” three consecutive times for project delivery.

This was an internal deployment supporting plant operations at CIPLA Ltd. The reported metrics are internal measurements rather than public product benchmarks.

MY JOURNEY

THE TREASURE MAP

From the classroom to applied AI work β€” tap a marker for the details.

Nov 2020 - May 2024
  • B.E. in Electronics and Instrumentation Engineering, CGPA 8.5/10.0, Bangalore Institute of Technology, Bengaluru.
  • Core subjects: Signal Processing, Control Systems, Embedded Systems, Data Structures, Engineering Mathematics.
July 2024 - June 2025
  • Official role: Trainee – Manufacturing & Formulation. Alongside that role, worked on RAG and anomaly-detection systems applied to plant operations.
  • Full breakdown - architecture, metrics, scope - is in the Experience section above.
June 2025 - May 2026
  • Coursework: Python, SQL, Deep Learning, NLP, MLOps, LLM Fine-tuning, Prompt Engineering, Model Deployment, Cloud Platforms.
2024 - 2025
  • "Performer of the Month" - CIPLA Ltd, three consecutive times, for consistent delivery on the RAG and anomaly-detection work described above.
SKILL ARENA

ABILITIES & EQUIPMENT

Core stack first β€” everything here has been used in the CIPLA role or one of the projects below.

Languages

PythonSQLCJava

Also worked with: PostgreSQL Β· Git / GitHub Β· Pandas / NumPy Β· CrewAI Β· ReAct Β· Streamlit Β· Power BI

MORE PROJECTS

ADDITIONAL PROJECTS

A selection of additional AI/ML projects β€” open one for the full breakdown.

Deep Learning Β· LLM Engineering

GPT from Scratch - PyTorch

Using pretrained models alone does not provide first-principles understanding of tokenization, attention, training dynamics, and autoregressive generation.

PyTorchCustom BPE TokenizerTransformerMulti-Head Attention
RAG Β· Developer Tools Β· Knowledge Systems

System Design RAG Assistant

Large system-design and software-engineering books are difficult to search conversationally, while ungrounded LLM responses can introduce incorrect technical information.

PythonQdrantSentence TransformersLLMPDF Processing
Voice AI Β· NLP Β· Enterprise Productivity

AI MOM - Meeting Minutes Generator

Manually transcribing meetings and creating structured minutes, decisions, and action items is repetitive and time-consuming.

WhisperPythonFastAPILLMsNLP
NLP Β· Developer Tooling Β· Database Intelligence

AskQL - Natural Language to SQL

Business users often understand the question they want answered but lack the SQL knowledge required to retrieve the data themselves.

PythonspaCyFastAPIReactLLM IntegrationSQL
Speech AI Β· NLP Β· Production Evaluation

ASR Shootout - Indian Conversational Speech

Traditional ASR benchmarks based only on WER do not always reveal how models behave on noisy, conversational, multilingual speech or important named entities.

WhisperGoogle STTDeepgramJiWERPandas
MLOps Β· Fraud Detection Β· Production ML

Fraud Detection MLOps Platform

Fraud models require more than model training: reproducibility, experiment tracking, deployment, monitoring, and reliable inference are essential for production use.

PythonLightGBMXGBoostMLflowDockerFastAPI
MLOps Β· Production AI Β· Model Monitoring

Fake News Trend Drift Detector

A deployed model can silently degrade when production data changes, without an automatic mechanism to detect drift or trigger retraining.

FastAPIMLflowDVCEvidently AIPrefectGrafanaDocker
Generative AI Β· Synthetic Data Β· Privacy

Synthetic Data Generation Platform

Real datasets can be difficult to share or use because of privacy constraints, while synthetic data must still preserve useful statistical relationships.

CTGANSDVPandasScikit-learnFastAPITypeScript
FinTech Β· Live Market Systems

AI-Powered Investment Portfolio Platform

Retail investors often lack integrated tools for live portfolio tracking, market analysis, alerts, and portfolio-level insights.

Next.jsPostgreSQLWebSocketsZerodha Kite ConnectJWT
Healthcare AI Β· Clinical Decision Support

AI Symptom Checker

Users often have difficulty interpreting symptoms or deciding what level of medical attention may be appropriate before consulting a professional.

PythonScikit-learnNLPRandom ForestGradient Boosting
Healthcare Β· Fraud Detection Β· ML

Health Insurance Fraud Detection System

Insurance fraud can involve subtle patterns across claims, providers, patients, and transaction attributes that are difficult to identify through simple rules.

PythonScikit-learnXGBoostPandasMachine Learning
Computer Vision Β· Infrastructure Monitoring

Structural Defect Analyzer

Manual infrastructure inspection is expensive and time-consuming, particularly when cracks and other visible defects must be identified across large structures.

PythonTensorFlowKerasOpenCVCNNTransfer Learning
Financial Risk Analytics Β· ML Β· Anomaly Detection

Credit Card Fraud Detection System

Fraudulent transactions represent a very small percentage of transactions, making raw accuracy misleading for fraud detection.

XGBoostScikit-learnSMOTEPandasMatplotlib
NLP Β· Recommendation Β· Recruitment AI

Resume Skill Matcher

Recruiters and applicants need a consistent way to compare resume skills with job requirements without relying only on keyword matching.

PythonNLPTF-IDFSentence TransformersSimilarity Scoring
Data Engineering Β· Monitoring Β· Analytics

Data Quality & Pipeline Monitoring Dashboard

Data pipelines can produce incomplete, inconsistent, or structurally incorrect datasets without providing clear visibility into the health of incoming data.

PythonPandasSQLData Quality ChecksPower BI
RESUME

πŸ“œ RESUME

My full experience and project history, in one document.

πŸ“œ

SHREYA BHAT

An ATS-friendly, one-page resume β€” always kept current. Replace public/assets/resume.pdf to update it everywhere on the site automatically.

⬇ DOWNLOAD PDF
MESSAGE HARBOR

πŸ“¬ SEND A MESSAGE

Have a project, opportunity, or idea in mind? I'd love to hear from you.

βœ‰shreyabhat545@gmail.com☎+91 9380040180πŸ™github.com/Shreyabhat11πŸ’ΌLinkedIn
πŸ“Bangalore, India

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