Jeevan Balagam
Staff AI Engineer at Ignite AI
San Jose, California, United States
Summary
👤
Senior🎓
Top SchoolJeevan Balagam is a Staff AI Engineer with 12 years of experience building production-grade ML, data engineering, and LLM-driven automation across manufacturing, healthcare, insurance, and analytics. He designs cost-efficient, explainable systems that turn complex business problems into measurable outcomes—cutting analysis times by over 95% in manufacturing and delivering multi-million-dollar warranty savings in field operations. Comfortable across the full stack, he deploys cloud-native pipelines (AWS/GCP/Azure), vector retrieval, and agentic workflows using tools like LangChain and LangGraph while also contributing optimizer and embedding work to notable open-source projects such as Neon and Factorie. His research blends practical engineering with rigorous evaluation—e.g., SLM-augmented reasoning that matched LLM accuracy at ~70% lower inference cost—and he has shipped end-to-end agentic systems for code repair and RCA assistance. Based in San Jose, he combines startup agility (founding AI engineer) with enterprise impact (Caterpillar), making him adept at translating ML research into scalable, business-ready products.
12 years of coding experience
9 years of employment as a software developer
Master of Science - MS, Artificial Intelligence, Master of Science - MS, Artificial Intelligence at San José State UniversityIndian Institute of Technology RoorkeePost Graduate Diploma, Data Science, Post Graduate Diploma, Data Science at International Institute of Information Technology Bangalore
Github Skills (25)
probabilistic-programming10
python10
optimizers10
data-modeling10
machine-learning10
optimizer10
word-embeddings10
probabilistic-reasoning10
scala10
lstm10
deep-learning10
statistical-models10
natural-language-processing10
probabilistic-models10
nlp10
Programming languages (2)
Github contributions (4)
NervanaSystems/neon
Sep 2015 - Nov 2015
Intel® Nervana™ reference deep learning framework committed to best performance on all hardware
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:8 commits in 1 month
Contributions summary:Jeevan implemented the Adagrad optimizer and added it to the neon framework, demonstrating contributions to the core optimization algorithms. They also added the LookupTable layer, a fundamental component for embedding-based models. Additionally, the user integrated and tested these new features within the existing framework, including tests for the Adagrad optimizer. Finally, the user added an IMDB review sentiment analysis example utilizing LSTM and LookupTable layers, indicating expertise in applying these technologies.
deep-learninghardwarepythonneonmkl
factorie/factorie
Mar 2014 - Apr 2014
FACTORIE is a toolkit for deployable probabilistic modeling, implemented as a software library in Scala. It provides its users with a succinct language for creating relational factor graphs, estimating parameters and performing inference.
Role in this project:Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:19 commits in 1 month
Contributions summary:Jeevan implemented core functionality related to word embeddings, a crucial component of probabilistic modeling within the Factorie toolkit. Their contributions include the development of `VocabBuilder`, and model components for word embedding tasks, including Skip-Gram and CBOW models using negative sampling. They also added features for loading, saving, and manipulating vocabularies. The user also made improvements by adding options like encoding, stop words, gzip for improved flexibility.
inferencescala
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