Srikanth Karri

Software Engineer

San Francisco, California, United States5 yrs 9 mos experience

Key Highlights

Expert in building secure, scalable backend systems.
Proven track record in LLM-based product development.
Strong experience in MLOps and cloud infrastructure.

Stackforce AI infers this person is a Backend-focused Software Engineer with expertise in MLOps and cloud infrastructure.

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Skills

Core Skills

Deep LearningSignal ProcessingNatural Language Processing (nlp)Data AnalysisCloud DevelopmentMlopsBackend DevelopmentMachine Learning

Other Skills

PySparkApache KafkaPython (Programming Language)SQLiteKubernetesAmazon Web Services (AWS)BashDockerTerraformGoogle Cloud Platform (GCP)JavaSpring BootREST APIsProduct DevelopmentBackend

About

I'm a software engineer with a strong focus on building secure, scalable backend systems—while also being comfortable working across the stack when needed. I'm always open to meaningful problems at the intersection of backend, infra, and product. Have been recently working with LLM based products. Active development with LLMs, Agentic systems, RAGs

Experience

5 yrs 9 mos

Total Experience

1 yr 9 mos

Average Tenure

3 mos

Current Experience

Servicenow

Software Engineer

Jan 2024 – Mar 2026 · 2 yrs 2 mos · Santa Clara, California, United States · Hybrid

Tgs

Cloud Developer Intern

Jun 2023 – Aug 2023 · 2 mos · Houston, Texas, United States · On-site

Implemented end-to-end MLOps framework from scratch, independently designing and setting up the infrastructure with DVC, Prefect, MLFlow, Grafana, etc, streamlining model deployment and management processes for the company.
Containerized applications using Docker and deployed them on Kubernetes clusters hosted on the Google Cloud Platform.
Built CI/CD pipelines using Azure DevOps and created Kubernetes clusters spanning multiple zones using Terraform (Infrastructure as Code) resulting in 20% deployment efficiency and increased availability of applications.
Proposed and implemented deploying workloads using helm charts with Terraform improving deployment efficiency by 30%.

KubernetesAmazon Web Services (AWS)BashMLOpsDockerTerraform+2

Virginia tech

2 roles

Graduate Research Assistant

Jan 2023 – Dec 2023 · 11 mos · Blacksburg, Virginia, United States · On-site

Developed a real-time data pipeline using Kafka and PySpark to extract data from signal sources, processing and converting it into spectrograms for a deep learning model.
Built a CNN LSTM model to classify the images extracted from the real-time data pipeline.
Improved the model's accuracy from 82% to 90% by optimizing the data pipeline using denoising and data compression.

Signal ProcessingPySparkApache KafkaDeep LearningPython (Programming Language)

Research Assistant

Aug 2022 – Jan 2023 · 5 mos · Blacksburg, Virginia, United States · On-site

Developed a Python-based web scraping application to extract disclosure data from a website and store it in an SQLite database for analysis and reporting purposes.
Applied Latent Dirichlet Allocation (LDA) modeling techniques to the disclosure data extracted. Generated topic models and identified trends and insights from the data for further analysis and reporting.
Used coherence and perplexity metrics to fine-tune the model and improve its effectiveness in identifying the number of topics.

SQLiteNatural Language Processing (NLP)Python (Programming Language)Data Analysis