π’ Open to Senior ML/AI Engineering roles & AI consulting opportunities
π Bengaluru, India Β β’Β β‘ Usually responds within 24h Β β’Β π ananttripathi.github.io/Anant-Portfolio
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"Building intelligent systems that don't just predict the futureβthey optimize it."
I'm a Senior ML & AI Engineer with 5+ years of experience building production-grade AI solutions across LLMs, optimization, and predictive analytics. Currently leading data science initiatives at Axtria β Ingenious Insights while pursuing 3 advanced AI/ML programs simultaneously (UT Austin, IIIT Bangalore, Deakin University).
What I Do:
- π§ Build and deploy GenAI applications using LLMs, RAG systems, and Azure OpenAI
- π― Architect marketing mix optimization platforms serving Fortune 500 pharma clients (Bayer, Merck, Novartis, Janssen)
- π Design scalable MLOps pipelines with Docker, MLflow, FastAPI, and CI/CD automation
- π Lead cross-functional teams delivering 25+ data science projects with measurable business impact
- π Mentor engineers and train 70+ professionals in ML, Python, SQL, and optimization strategies
- ποΈ Own 10+ product capabilities from design to deployment with enterprise-scale impact
Career Highlights:
- π 4 promotions in 3.5 years: Analyst β Associate β Senior Associate β Project Leader
- β‘ 98-100% error-free delivery rate across production releases
- π― 95%+ on-time delivery for 10+ major product capabilities
- π‘ Led GenAI integration using Azure OpenAI improving user engagement by 40%
- π Reduced execution time by 72% and memory consumption by 63%
- π Increased HCP adoption rates by 38% and model accuracy by 35%
| Project | What | Status |
|---|---|---|
| RAG-based Medical Assistant | Deploying ChromaDB + Mistral 7B medical Q&A to Hugging Face Spaces | π‘ In Progress |
| File Whisperer v2 | Adding multi-doc support and streaming responses | π‘ In Progress |
| Deakin MDS Program | Advanced data science coursework: analytics, modeling, business insights | π’ Active |
| IIIT Bangalore: Agentic AI | Multi-agent systems, LLM orchestration, tool-use patterns | π’ Active |
| UT Austin: AI/ML PGP | Capstone project: production ML system design | π’ Active |
ποΈ Last updated: April 2026
Your support helps me create more open-source projects and share knowledge with the community.
| Metric | Achievement | Domain |
|---|---|---|
| Performance Optimization | 72% reduction in execution time | Algorithm Engineering |
| Memory Efficiency | 63% decrease in consumption | Enterprise Data Pipelines |
| Business Impact | 38% increase in adoption rates | Predictive Analytics |
| Model Accuracy | 35% improvement in precision | HCP Targeting Models |
| Leadership | Trained 70+ professionals | Python, SQL, Optimization |
| Project Delivery | 25+ successful deployments | Healthcare & Marketing |
| Team Management | Led 5+ data scientists | Cross-functional Collaboration |
| API Architecture | Built Pre/Post-Optimization APIs | System Design & Scalability |
Specializations: Machine Learning β’ Deep Learning β’ Predictive Analytics β’ Statistical Modeling β’ Feature Engineering β’ Time Series Forecasting β’ Computer Vision β’ NLP
Expertise: RAG Systems β’ Prompt Engineering β’ LLM Fine-Tuning β’ Embeddings β’ Semantic Search β’ Inference Optimization β’ LlamaIndex
Vector Databases: FAISS β’ Pinecone β’ Weaviate
Tech Stack: Python β’ Optimization Algorithms β’ Azure β’ MLOps β’ SaaS
- Led development of enterprise-scale Marketing Mix Modeling framework for Fortune 500 pharma clients
- Architected 10+ optimization capabilities including Portfolio Optimization, Multi-Level Constraints, and Monthly Gating
- Implemented advanced algorithms (COBYLA, SLSQP, etc.) with non-linear response modeling
- Delivered 25+ MMM projects for Bayer, Merck, Novartis, Janssen with measurable ROI improvements
- Built Pre/Post-Optimization APIs reducing execution time by 72% and memory by 63%
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Upload any PDF, DOCX, or TXT and chat with it using AI. RAG-powered document Q&A with FastAPI backend, pgvector semantic search, and Cohere embeddings. Supports BYOK (Bring Your Own Key). Stack: Python β’ FastAPI β’ LangChain β’ pgvector β’ Cohere β’ React β’ Vercel |
Fast, lightweight URL shortener with click analytics, custom aliases, and link expiry. Node.js + Express backend on Hugging Face Spaces, PostgreSQL on Neon, frontend on Vercel. Stack: Node.js β’ Express β’ PostgreSQL β’ Neon β’ Vercel β’ Hugging Face |
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Client-side code & text diff tool β paste or upload files, compare with syntax-aware highlighting. Supports Jupyter notebooks, privacy-first (no data sent to server). Stack: JavaScript β’ HTML5 β’ CSS3 β’ GitHub Pages |
Web app converting YAML configurations to Python variable assignments in real-time. Privacy-first, fully client-side processing with js-yaml. Stack: JavaScript β’ js-yaml β’ HTML5 β’ CSS3 β’ GitHub Pages |
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End-to-end MLOps pipeline for predicting customer purchase of wellness tourism packages. XGBoost classification with MLflow tracking, Hugging Face data/model versioning, GitHub Actions CI/CD, and Dockerized Streamlit deployment. Stack: Python β’ XGBoost β’ MLflow β’ Docker β’ GitHub Actions β’ Streamlit β’ Hugging Face |
End-to-end MLOps pipeline for engine failure classification using 6 sensor inputs (RPM, oil/fuel/coolant pressure, temperature). MLflow experiment tracking, GitHub Actions CI/CD, and Dockerized Streamlit deployment on Hugging Face Spaces. Stack: Python β’ Scikit-learn β’ XGBoost β’ MLflow β’ Docker β’ GitHub Actions β’ Streamlit β’ Hugging Face |
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End-to-end deep learning system for detecting pneumonia from chest X-rays. Trained on the RSNA dataset (26,000+ images) using EfficientNetB3 transfer learning. Supports DICOM and standard image formats with confidence scoring and clinical recommendations. Model Performance: 74.76% validation accuracy Β· 3-class classification (Normal / Lung Opacity / Not Normal) Stack: Python β’ TensorFlow β’ EfficientNetB3 β’ CNN β’ Transfer Learning β’ Streamlit β’ Docker β’ Hugging Face Hub |
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RAG-based medical Q&A over the Merck Manual (19th ed.). ChromaDB semantic search, GTE-large embeddings, Mistral 7B (GGUF) for answer generation. Runs fully locally for privacy with optional GPU acceleration. Stack: Python β’ LangChain β’ ChromaDB β’ Mistral β’ Sentence-Transformers β’ Jupyter |
RAG-powered HR policy Q&A bot for Flykite Airlines employee handbook. Answers employee questions from a PDF knowledge base with page-level citations. Deployed on Hugging Face Spaces with GitHub Actions CI/CD auto-deploy pipeline. Stack: Python β’ LangChain β’ FAISS β’ Groq (LLaMA 3.3 70B) β’ sentence-transformers β’ Gradio β’ GitHub Actions |
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Agentic AI chatbot for food-delivery order support. A SQL agent queries a live orders database and an LLM formats the response into natural, empathetic replies. Includes guardrails for blocked queries and automatic escalation to human agents. Stack: Python β’ LangChain β’ Groq (LLaMA 4) β’ SQLite β’ SQL Agent β’ Gradio β’ GitHub Actions |
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Marketing Mix Modelling app: attribute sales/revenue to channels with adstock, saturation transforms, and ROI/mROI. Streamlit wizard, 5 model types (Linear, Ridge, Lasso, Bayesian), segment analysis. Stack: Python β’ Streamlit β’ Scikit-learn β’ Bayesian β’ Optimization |
AI-powered MLOps platform that optimizes your resume for Applicant Tracking Systems. ATS scoring, keyword analysis, skill gap insights, and smart job matching. Stack: Python β’ NLP β’ MLOps β’ Streamlit β’ AI |
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Interactive roadmap for Data Engineer, Data Scientist, ML Engineer, AI Engineer paths. Progress tracking, clickable topics with resources, study schedules, and interview prep. Stack: HTML β’ CSS β’ JavaScript β’ GitHub Pages |
Free, comprehensive learning platform for mastering Data Science, AI, and ML. 445+ curated problems across 16 topics: Python, ML, Deep Learning, NLP, Computer Vision, and more. Stack: HTML β’ JavaScript β’ Problem-solving β’ Education |
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Professional portfolio website: ML/AI projects, Generative AI & MLOps experience, marketing analytics, and product optimization. Apple-inspired design, responsive, FormSubmit contact. Stack: HTML5 β’ CSS3 β’ JavaScript β’ GitHub Pages |
Comprehensive AI & ML project portfolio from University of Texas at Austin PG Program. Real-world data science and machine learning solutions across multiple domains. Stack: Jupyter β’ Python β’ Scikit-learn β’ Neural Networks β’ MLOps |
| Project | Description |
|---|---|
| MDS-Deakin-University | Data science projects from Deakin University MDS program β analytics, modeling, business insights |
| PGP-Applied-AI-Agentic-AI-IIITB | Applied AI & Agentic AI from IIIT Bangalore β LLMs, RAG, multi-agent systems |
| System-Design | System design roadmaps for SDE, ML Engineer, AI Engineer, Data Scientist, Data Engineer |
| Anant-Tripathi | Cyberpunk-inspired portfolio with particle animation |
Career Progression (4 promotions in 3.5 years):
Project Leader β Data Science / ML (May 2024 β Present)
- Leading 10+ major product capabilities with 95%+ on-time delivery and 98-100% error-free releases
- Architecting scalable optimization systems serving enterprise pharmaceutical clients
- Mentoring team of 5+ data scientists and training 70+ employees
Senior Associate β Data Scientist (May 2023 β Apr 2024)
- Owned MMX optimization enhancements and algorithm implementations (COBYLA, SLSQP, CCSA)
- Led high-impact POCs including Grid Selection, LSTM forecasting, and execution time optimization
- Supported multiple global projects for Novartis brands across Poland and Germany
Associate β Data Scientist (May 2022 β Apr 2023)
- Delivered client-specific enhancements for Janssen and Novartis with custom segmentation
- Designed performance-optimized workflows improving memory utilization significantly
- Researched and validated SLSQP algorithm implementation for Optimization API
Analyst β Data Scientist (Jul 2021 β Apr 2022)
- Built Early Adopter Predictor increasing HCP targeting adoption by 38%
- Delivered 5 Marketing Mix Modeling projects for top US pharma clients
- Established foundation in MMM techniques and analytics workflow delivery
- π Deakin University, Australia | Masters of Data Science (Jun 2026 β Jun 2027)
- π International Institute of Information Technology, Bangalore | Executive PGP in Applied AI & Agentic AI (Dec 2025 β Aug 2026)
- π The University of Texas at Austin, USA | Post Graduate Program in Artificial Intelligence & Machine Learning (Feb 2025 β Mar 2026)
- π Birla Institute of Technology and Science, Pilani | B.E. & M.Sc. (Integrated) in Electrical and Electronics (Aug 2016 β Jun 2021)
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Machine Learning Specialization β Stanford University & Deeplearning.ai (Andrew Ng)
- Comprehensive coursework in supervised/unsupervised learning, neural networks, and ML best practices
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Generative AI for Software Developers β IBM
- Practical applications of GenAI in software engineering workflows
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Introduction to Generative AI β Google Cloud
- Core concepts and cloud deployment of GenAI solutions
- π Right Brigade Award (Axtria) β Recognized for exemplary display of "RIGHT" values: Responsiveness, Integrity, Get going, Humble, and Team Player
- π Bravo Award (Axtria) β Honored for delivering high-quality work, exemplary performance, and strong client appreciation across multiple high-stakes projects
current_focus = {
"research": [
"Agentic AI Systems",
"RAG Architectures & Vector Search",
"LLM Fine-Tuning & Inference Optimization",
"Multi-Agent Coordination"
],
"engineering": [
"MLOps Pipelines & Automation",
"System Architecture & API Design",
"Optimization Algorithms (COBYLA, SLSQP, CCSA)",
"Real-time Model Serving"
],
"business": [
"Marketing Mix Modeling (MMM)",
"Portfolio Optimization",
"Product Leadership & Strategy",
"Enterprise AI Solutions"
],
"learning": [
"Advanced AI/ML Research (UT Austin)",
"Applied AI & Agentic Systems (IIIT Bangalore)",
"Data Science Mastery (Deakin University)",
"Distributed Computing & Cloud Architecture"
],
"teaching": [
"Training 70+ professionals",
"Technical mentorship",
"Knowledge sharing & documentation"
]
}- Azure OpenAI integration and production deployment
- RAG system architecture with vector databases (FAISS, Pinecone, Weaviate)
- Prompt engineering and LLM fine-tuning
- Embeddings and semantic search optimization
- LangChain and LlamaIndex workflows
- Marketing Mix Modeling (MMM) with 25+ delivered projects
- Advanced optimization algorithms: COBYLA, SLSQP, CCSA
- Non-linear response curves (S-curves, diminishing returns)
- Portfolio-level optimization with multi-level constraints
- Budget planning and profit maximization scenarios
- Supervised learning: Random Forest, XGBoost, Logistic Regression
- Time series forecasting and anomaly detection
- Early adopter prediction and HCP targeting
- A/B testing, experiment design, and causal inference
- Model evaluation and hyperparameter optimization
- End-to-end pipeline automation with CI/CD
- Docker containerization and FastAPI deployment
- MLflow for experiment tracking and model versioning
- Cloud deployment: AWS, Azure, GCP, Databricks
- Performance optimization: 72% execution time reduction, 63% memory reduction
Your support helps me create more open-source projects and share knowledge with the community.
I'm always interested in:
- π Collaborating on AI/ML projects
- π‘ Discussing GenAI, LLMs, and optimization strategies
- π Sharing knowledge on MLOps and production ML systems
- π― Exploring opportunities in ML Engineering and AI Research
Reach out:
βοΈ From ananttripathi - Building the future of AI, one model at a time







