ENGINEER
AI-NATIVE
PRODUCTS.
I build reliable RAG systems, scalable MLOps pipelines, and intelligent products that empower businesses to lead in the AI era.
Saran Jaya Thilak
AI Engineer
About Me
I engineer data
and AI systems
that scale.
From building the foundations at Tesla and Nokia to architecting the future of GenAI, I specialize in bridging the gap between raw data and production-grade intelligence.
Armed with an M.Sc. in Data Science, I thrive on turning complex technical challenges into seamless, high-performance solutions. Whether it's designing robust data pipelines, implementing RAG systems, or deploying scalable MLOps architectures, my goal is simple: to build something incredible.
Experience
Nine years, four teams.
Tesla
Data Engineer
Huawei
Cloud Support Engineer
Huawei
Operations Team Leader
Nokia
Operations & Maintenance Engineer
Expertise
Data Engineering
Designing and running production data pipelines with Airflow and DBT, moving and modeling data reliably at scale on BigQuery.
GenAI & RAG Systems
Building retrieval-augmented generation systems end to end — ingestion, embeddings, vector search, and streaming LLM responses — with LangChain and modern inference APIs.
MLOps
Standing up model registries, experiment tracking, and versioned deployments so machine learning systems ship and stay reliable in production.
Cloud Architecture
Designing secure, scalable infrastructure on AWS, drawing on prior cloud and networking engineering work at Tesla, Nokia, and Huawei.
Full-Stack Development
Shipping the frontend and backend both — from FastAPI services to React interfaces — so a system is usable end to end, not just functional.
Projects
Open Source
GitHub Contributions
Credentials
Certifications.
Professional Certifications
AWS Certified Solutions Architect
SQL for Data Science
OCI 2025 Certified Generative AI Professional
OCI 2025 Certified AI Foundations Associate
Research
Published work.
Publications
Performance Ratio Estimation of Solar Power Plants Using Machine Learning Algorithms
Applied advanced machine learning models to estimate the performance ratio of solar PV plants using operational and environmental data. Among tested algorithms, Random Search CV Regression achieved the highest accuracy with an R² of 0.93 and minimal error. Results highlight the effectiveness of models like Random Search CV, AdaBoost, and Random Forest in optimizing solar plant performance and advancing efficient renewable energy solutions.
A Comparison Between Machine Learning Models for Air ticket Price Prediction
Explored machine learning techniques to predict airline ticket prices using two Kaggle datasets, addressing challenges from dynamic pricing fluctuations. Evaluated models including Random Forest and Randomized Search CV, with the proposed model providing accurate fare predictions. This solution supports travelers in making informed decisions by forecasting price trends from historical data.
Design.
Disrupt.
Conquer.
We build reliable data engineering & generative AI systems that don't just turn heads — they lead.
- Phone
- +49 174 461 4592
- GitHub
- github.com/saranjthilak
- linkedin.com/in/saranjayathilak