Prasanna Patil

Software Engineer

Bengaluru, Karnataka, India5 yrs 8 mos experience

Highly Stable

Key Highlights

Expert in productionizing large language models.
Innovative solutions for named-entity recognition.
Strong commitment to continuous learning.

Stackforce AI infers this person is a Machine Learning Engineer specializing in E-commerce and AI-driven solutions.

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Skills

Core Skills

Large Language Models (llm)Deep Neural Networks (dnn)

Other Skills

C++TensorFlowSoftware DevelopmentMachine LearningDeep LearningPython (Programming Language)

About

Machine Learning Engineer specializing in Applied AI/ML, leveraging cutting-edge research to craft innovative, user-centric solutions. Proven expertise in productionising and optimizing (large) language models for enhancing named-entity recognition and result relevance / recommendation systems. Committed to continuous hands on learning and acquiring new skills. Eager to collaborate and drive impactful product capabilities.

Experience

5 yrs 8 mos

Total Experience

4 yrs 8 mos

Average Tenure

1 yr

Current Experience

Google deepmind

Software Engineer

Apr 2025 – Present · 1 yr · Hybrid

Google

Software Engineer

Aug 2020 – Apr 2025 · 4 yrs 8 mos · Bengaluru, Karnataka, India

Large Language Models (LLM)C++

Amazon

Applied science intern

May 2019 – Jul 2019 · 2 mos · Bengaluru Area, India

My project involved in developing a neural ranking approach for personalization of amazon.com/spark.
1. Worked on scaleable and efficient random walk based algorithms for user recommendation personalization which are currently used by the likes of Twitter and Pinterest for the recommendation.
2. Experimented with various recommendation algorithms such as Personalized PageRank, SimRank, HITS, Random Walks with Restarts, GraphJet, Pixie like approaches.
3. Developed a neural ranking approach based on graph autoencoder which uses topological features along with side information along with multitasking learning using recent advances in representation learning leading to 10% improvement in ROC scores in offline evaluation over baseline.

TensorFlowDeep Neural Networks (DNN)