Richa Sinha

Software Engineer

Bengaluru, Karnataka, India13 yrs 10 mos experience

AI ML PractitionerHighly Stable

Key Highlights

Expert in Generative AI and text-to-image synthesis.
Proficient in end-to-end machine learning pipeline development.
Strong background in both software engineering and machine learning.

Stackforce AI infers this person is a Senior Fullstack Engineer specializing in AI and machine learning solutions.

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Skills

Core Skills

Machine LearningArtificial Intelligence (ai)

Other Skills

NumPyElastic searchTensorFlowPyTorchParquetMDSSmall Team ManagementElixirSpring FrameworkKubernetesJavaXMLCore JavaJSPSQL

About

As a Senior ML Fullstack Engineer, my focus lies in Generative AI and text-to-image synthesis. With a profound understanding of both machine learning and software engineering, I craft cutting-edge solutions that seamlessly merge language and visual domains. My passion drives me to explore AI's creative possibilities, particularly in generating high-quality images from textual inputs. From data preprocessing to model training, scalable deployment, and user-friendly interfaces, my expertise spans the entire ML pipeline. Thriving in dynamic settings, I consistently deliver innovative solutions that redefine the frontiers of AI technology.

Experience

13 yrs 10 mos

Total Experience

3 yrs 1 mo

Average Tenure

1 yr 3 mos

Current Experience

Google

Staff Software Engineer

Mar 2025 – Present · 1 yr 3 mos

Adobe

3 roles

Software Development Engineer 5

Promoted

Feb 2024 – Feb 2025 · 1 yr

Spearheaded the development of a sophisticated system dedicated to monitoring the end-to-end flow of data throughout our machine learning pipeline.
o Leveraging the powerful features of W&B, I orchestrated the implementation of robust tracking mechanisms, enabling real-time insights into model performance, data quality, and pipeline efficiency.
o This initiative empowered our team to make informed decisions, optimize workflows, and drive continuous improvement in our machine learning processes.
Migrated numerous models to our data platform to generate embedding outputs in MDS shards format.
o Due to the substantial volume of output from these models, I dedicated efforts to optimize their performance, ensuring efficient processing of millions of data entries within tight timeframes.
Managed the integration of data into our training platform, sourced from various channels.
o The data underwent preprocessing steps such as cropping and aspect ratio adjustments before being passed through multiple inference models.
o Subsequently, the data was transformed into embeddings compatible with training models.
o Additionally, I established a visualization framework to validate outputs for large datasets.

Machine LearningArtificial Intelligence (AI)