My engineering journey started in the physical world. With a background in Mechanical Engineering and a recognized passion for illustration (honored with a National Award from the President of India in 2012 & 2017), I have always loved breaking down how complex systems fit together.

Today, I apply that same spatial reasoning and structural mindset to distributed cloud systems, DevOps pipelines, and Site Reliability Engineering (SRE). I specialize in automating away operational toil, securing infrastructure, and managing highly reliable enterprise environments (currently managing 50+ distributed compute nodes and high-throughput event streams).

Core Infrastructure & DevOps:

Languages, Backend & Databases:

System Tools & Environments:

AI & GenAI:

Foundations → APIs → Prompting → Local LLMs → Agents → RAG → Async Queues → Multimodal → LangGraph → Memory (Vector + Graph) → Voice

Since transitioning into a dedicated enterprise DevOps role, I have focused heavily on eliminating operational toil through automation. My recent workflow architectures have generated the following verified time-savings:

• Automated Post-Deployment Validation: Replaced a manual log-checking checklist with an AWS-native Python engine.
  • Impact: Reduced QA sign-off time from 30 minutes to under 5 minutes.
• Zero-Touch UAT Health Checks: Engineered an automated health-check pipeline to validate environment integrity without requiring senior engineer intervention.
  • Impact: Reduced environment validation time from 1.5 hours to ~5 minutes.
• 1-Click Vulnerability Remediation (DevSecOps): Designed a CI/CD workflow that handles environment setup, terminal execution, and deployment for patching CVEs.
  • Impact: Reduced critical patching lifecycle from 1 full day of manual effort to 30 minutes of automated background processing.
• Event-Driven Data Seeding: Automated manual S3 bucket data-dump processes that previously required data analytics and SQL expertise.
  • Impact: Reduced trigger time from 30 minutes to 2 minutes, democratizing the process for non-data engineers.

I believe in understanding why a technology exists, not just how to use it. When I am not building infrastructure, I am deep-diving into the mechanics of next-generation tools:

• Event Streaming at Scale: Researching how massive platforms handle throughput bottlenecks beyond standard Kafka implementations.
• Deep OS Troubleshooting: Going beyond standard administration by utilizing strace for system calls, tracking inode allocations, and performing packet forensics with tcpdump and Wireshark.
• Version Control Under the Hood: Analyzing Meta’s Sapling to understand how data structures and historical node snapshots operate differently from standard Git architecture.

• Orchestration: Transitioning deeper into container orchestration by exploring Kubernetes (K8s) and multi-stage Docker optimization.

• Backend Engineering: Expanding my architectural scope by building Python-based web backends (including bypassing frameworks to build raw HTTP servers using basic TCP sockets).

• Algorithms: Consistently sharpening my problem-solving efficiency and data structures knowledge.

• Agentic AI & GenAI Engineering: Building production-grade GenAI systems. Experience includes LangGraph-based agentic workflows, hybrid memory architecture (Semantic/Episodic via Qdrant + Relational via Neo4j & mem0), scalable asynchronous RAG pipelines using Redis/Valkey message queues, and building Multimodal/Voice-controlled agents using OpenAI and local (Ollama) models.

Catch up on my latest algorithmic problem-solving:

Note: Due to strict corporate NDAs and security policies, specific enterprise project code is maintained in private repositories. Below are high-level overviews of the architectural challenges I solve in production environments.

• Zero-Touch QA Validation Engine: Engineered an AWS-native Python automation tool using boto3 to dynamically discover cloud resources at runtime. Integrated with CloudWatch and Datadog to automate deployment health checks, reducing manual sign-off time from ~30 minutes to under 2 minutes while enforcing a zero-error baseline.
• High-Throughput Data Pipelines: Manage secure, event-driven pipelines (Amazon MSK/Kafka) and public-facing APIs, ensuring reliable delivery for downstream B2B applications processing 200 GB to 500 GB of data daily.

• Proactive System Monitoring: Lead Platform L2 shift support by managing sophisticated Datadog monitors across 5 environments, tracking compute capacity, broker health, and cluster utilization to maintain near-zero deployment downtime.
• Cost Anomaly Root Cause Analysis: Triaged runaway AWS billing alerts by cross-referencing CloudWatch metrics with CloudTrail logs. Identified and severed an infinite synchronous retry loop within an event-streaming architecture, proposing permanent architectural safeguards.

• Vulnerability Remediation (CVEs): Spearheaded DevSecOps remediations to patch critical vulnerabilities in containerized serverless deployments. Implemented multi-stage Docker builds to harden security and achieved a 42% image size reduction (607MB to 350MB).
• IAM Least-Privilege Enforcement: Authored and deployed Terraform configurations to systematically re-route AWS IAM permissions and manage VPC endpoints, ensuring seamless transitions without service interruption.

TCS Gems — Star of the Month Award

Awarded by Tata Consultancy Services | July 2026

"In appreciation of your outstanding contribution to the organisation"
— Sudeep Kunnumal, Chief Human Resources Officer, TCS

  

"Success looks good on paper, but experiences truly make you feel alive. Fail, learn, and repeat."

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📂 Personal Archive & Academic Projects: (2021 - 2025)

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