Deepak K.

I am a Full Stack Node , Express , Next, React , AI/ML Developer, DevSecOps Engineer with over 12 years of development experience, 5 years in Cloud DevOps, Including more than 3 years specializing in secure and automated GenAI and AI/ML deployments. I help organizations deploy and manage LLM-based and agentic AI applications securely on both cloud and on-premises environments using DevSecOps and AIOps best practices. My expertise spans secure agentic systems infrastructure, MLOps, containerization of LLM workloads, integration of monitoring and alerting, and end-to-end security implementation across CI/CD and production environments. What I Do: • Deploy GenAI/LLM-based applications on AWS, Azure, GCP, or on-prem with Docker and Kubernetes • Design and manage infrastructure for agentic systems (LangChain, AutoGPT, RAG apps) • Implement security automation across AI/ML pipelines using tools like SonarQube, OWASP Dependency Check, AWS WAF, IAM • Develop CI/CD pipelines for AI models and inference services with Jenkins, GitLab, GitHub Actions • Containerize AI models and agents for reproducible, scalable deployment • Deploy and manage vector databases like Qdrant, Pinecone, and Weaviate • Set up monitoring and alerting for GenAI applications using Prometheus and Grafana • Implement DevSecAIOps workflows that include security, observability, and performance monitoring Technical Skills: - Cloud Platforms: AWS, Azure, GCP, DigitalOcean - Infrastructure as Code: Terraform, CloudFormation - CI/CD: Jenkins, GitHub Actions, GitLab CI - Containerization: Docker, Kubernetes, ECS - AI/ML & GenAI: LangChain, LlamaIndex, OpenAI API, Hugging Face, FastAPI, Streamlit - Monitoring: Prometheus, Grafana, CloudWatch - Security: IAM, AWS Inspector, WAF, OWASP, SonarQube, Nexus3, Dependency Scanning - Languages: Python, Bash - Version Control: Git - Others: JIRA, Confluence, Cert-Manager, API Gateway Past Projects: 1. Deployed secure, containerized GenAI agents using LangChain and FastAPI on AWS, with IAM policies, WAF, and Kubernetes-based autoscaling. 2. Created cross-cloud DevSecOps pipelines for ML models using Jenkins, GitHub Actions, SonarQube, and Terraform. 3. Designed on-prem agentic stack with Docker, Kubernetes, custom ingress, and vector DB integration. 4. Built an AIOps observability layer for LLM agents using Prometheus, Grafana, and Lambda for auto-remediation. If you're building GenAI-powered applications, LLM-based systems, or scalable AI infrastructure and want to ensure everything is secure, automated, and production-ready — I can help. Thank you! Deepak Kumar