Ranjeet Singh

Software Engineer

Bengaluru, Karnataka, India2 yrs 11 mos experience

Highly Stable

Key Highlights

Expert in optimizing CI/CD pipelines and Kubernetes management.
Developed innovative AI solutions for IoT and data analysis.
Certified in Python, SQL, Machine Learning, and Deep Learning.

Stackforce AI infers this person is a SaaS-focused Software Engineer with expertise in AI and cloud technologies.

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Skills

Core Skills

Machine LearningKubernetesNatural Language Processing (nlp)ClusteringContinuous Integration And Continuous Delivery (ci/cd)Devops

Other Skills

AdvertisingAzure Kubernetes Service (AKS)BashCascading Style Sheets (CSS)ConsulDecision-MakingDeep LearningDensity based clusteringDigital MarketingDockerEDAFlaskGPTGitGitFlow

About

I am a Software Engineer with expertise in deep learning, machine learning, and full-stack development, specializing in optimizing CI/CD pipelines and managing Kubernetes clusters. I thrive on solving complex challenges and aligning tech solutions with business goals. Notable projects include a Twitter Sentiment Analysis tool, an AI-driven Book Recommendation System, and Text Summarization applications. I'm passionate about innovation and continuous learning, and I am certified in Python, SQL, and Machine Learning, Deep Learning. Let’s connect to build impactful solutions! 🚀

Experience

2 yrs 11 mos

Total Experience

2 yrs 11 mos

Average Tenure

Current Experience

Smarthub.ai

2 roles

Software Engineer - II ( Machine Learning )

Mar 2024 – Feb 2025 · 11 mos · Bengaluru, Karnataka, India

Developed a K-Means++ clustering pipeline to analyze historical IoT alerts, reducing redundant notifications by 30% .

Deep LearningMachine LearningKubernetesMicrosoft AzureClusteringGithub workflow+12

Software Engineer ( Machine Learning )

Feb 2022 – Feb 2024 · 2 yrs · Bengaluru, Karnataka, India

Built an AI-powered chatbot using LangChain and GPT for IoT device support, reducing query resolution time by 50%. Leveraged ChromaDB and Pinecone for efficient vector search.
Automated service deployment using Helm charts, reducing setup time by 30%. Configured Consul for high availability, eliminating single points of failure.
Optimized CI/CD pipelines with GitHub Actions, reducing deployment time by 50% and decreasing errors by 30%.
Managed Azure AKS clusters, optimizing cloud costs and achieving a 5% cost savings.

Deep LearningMachine LearningKubernetesMicrosoft AzureClusteringGithub workflow+12