"Building intelligent systems that don't just predict the future—they 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 – Ingenious Insights while pursuing 3 advanced AI/ML programs simultaneously (UT Austin, IIIT Bangalore, Deakin University).
What I Do:
- 🧠 Build and deploy GenAI applications using LLMs, RAG systems, and Azure OpenAI
- 🎯 Architect marketing mix optimization platforms serving Fortune 500 pharma clients (Bayer, Merck, Novartis, Janssen)
- 🚀 Design scalable MLOps pipelines with Docker, MLflow, FastAPI, and CI/CD automation
- 📊 Lead cross-functional teams delivering 25+ data science projects with measurable business impact
- 🎓 Mentor engineers and train 70+ professionals in ML, Python, SQL, and optimization strategies
- 🏗️ Own 10+ product capabilities from design to deployment with enterprise-scale impact
Career Highlights:
- 🏆 4 promotions in 3.5 years: Analyst → Associate → Senior Associate → Project Leader
- ⚡ 98-100% error-free delivery rate across production releases
- 🎯 95%+ on-time delivery for 10+ major product capabilities
- 💡 Led GenAI integration using Azure OpenAI improving user engagement by 40%
- 🚀 Reduced execution time by 72% and memory consumption by 63%
- 📈 Increased HCP adoption rates by 38% and model accuracy by 35%
| 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 • Deep Learning • Predictive Analytics • Statistical Modeling • Feature Engineering • Time Series Forecasting • Computer Vision • NLP
Expertise: RAG Systems • Prompt Engineering • LLM Fine-Tuning • Embeddings • Semantic Search • Inference Optimization • LlamaIndex
Vector Databases: FAISS • Pinecone • Weaviate
Tech Stack: Python • Optimization Algorithms • Azure • MLOps • 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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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 • XGBoost • MLflow • Docker • GitHub Actions • Streamlit • Hugging Face |
IoT-based predictive maintenance using engine sensor data. Time-series feature engineering, Random Forest & Gradient Boosting models, and automated evaluation pipeline with ROC-AUC, F1-score tracking. Stack: Python • Scikit-learn • XGBoost • Time Series • Hugging Face • Streamlit |
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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 • LangChain • ChromaDB • Mistral • Sentence-Transformers • Jupyter |
RAG-powered HR policy Q&A bot for Flykite Airlines employee handbook. Query via natural language with LangChain, FAISS/ChromaDB, OpenAI/Claude. Source attribution and hyperparameter tuning. Stack: LangChain • FAISS • ChromaDB • OpenAI • Claude • RAG |
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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 • Streamlit • Scikit-learn • Bayesian • 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 • NLP • MLOps • Streamlit • 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 • CSS • JavaScript • 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 • JavaScript • Problem-solving • 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 • CSS3 • JavaScript • 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 • Python • Scikit-learn • Neural Networks • MLOps |
| Project | Description |
|---|---|
| MDS-Deakin-University | Data science projects from Deakin University MDS program — analytics, modeling, business insights (⭐ 7) |
| PGP-Applied-AI-Agentic-AI-IIITB | Applied AI & Agentic AI from IIIT Bangalore — LLMs, RAG, multi-agent systems (⭐ 5) |
| System-Design | System design roadmaps for SDE, ML Engineer, AI Engineer, Data Scientist, Data Engineer |
| Anant-Tripathi | Cyberpunk-inspired portfolio with particle animation (⭐ 6) |
| Rough | Lightweight sandbox for experiments and scratch work |
Career Progression (4 promotions in 3.5 years):
Project Leader – Data Science / ML (May 2024 – 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 – Data Scientist (May 2023 – 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 – Data Scientist (May 2022 – 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 – Data Scientist (Jul 2021 – 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
- 🎓 Deakin University, Australia | Masters of Data Science (Jun 2026 – Jun 2027)
- 🎓 International Institute of Information Technology, Bangalore | Executive PGP in Applied AI & Agentic AI (Dec 2025 – Aug 2026)
- 🎓 The University of Texas at Austin, USA | Post Graduate Program in Artificial Intelligence & Machine Learning (Feb 2025 – Mar 2026)
- 🎓 Birla Institute of Technology and Science, Pilani | B.E. & M.Sc. (Integrated) in Electrical and Electronics (Aug 2016 – Jun 2021)
- ✅ Machine Learning Specialization – Stanford University & Deeplearning.ai (Andrew Ng)
- Comprehensive coursework in supervised/unsupervised learning, neural networks, and ML best practices
- ✅ Generative AI for Software Developers – IBM
- Practical applications of GenAI in software engineering workflows
- ✅ Introduction to Generative AI – Google Cloud
- Core concepts and cloud deployment of GenAI solutions
- 🏅 Right Brigade Award (Axtria) – Recognized for exemplary display of "RIGHT" values: Responsiveness, Integrity, Get going, Humble, and Team Player
- 🏅 Bravo Award (Axtria) – 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
I'm always interested in:
- 🚀 Collaborating on AI/ML projects
- 💡 Discussing GenAI, LLMs, and optimization strategies
- 📚 Sharing knowledge on MLOps and production ML systems
- 🎯 Exploring opportunities in ML Engineering and AI Research
Reach out:
⭐️ From ananttripathi - Building the future of AI, one model at a time