Programming Fundamentals
Data structures, algorithms, clean code, modular design, and problem solving.
Senior Software Engineer | Backend systems and production reliability
Lokesh Burma
Backend Software Engineer with 5 years of experience building SaaS backend systems, improving reliability, working with databases, and debugging production issues. I focus on clean APIs, scalable services, observability, and practical AI-assisted engineering.
Engineering impact
Built backend routing logic for large-scale SaaS data workflows with phased rollout controls.
Improved reliability by classifying temporary, permanent, and customer-action-needed failures.
Preserved retry history and error context to make production debugging easier.
Built AI-assisted debugging workflows using logs, issue context, database checks, and RCA prompts.
Positioning
Backend engineer focused on reliable SaaS systems.
I focus on backend APIs, databases, system design, production debugging, observability, cloud delivery, and practical AI-assisted engineering.
SaaS Backend Architecture
Built backend features for multi-tenant SaaS systems with attention to data correctness, safe rollout, and maintainable service behavior.
Reliability And Debugging
Improved failure handling by making errors easier to classify, retry, inspect, and explain during production support.
AI-Assisted Engineering
Used AI tools to support debugging and RCA by combining logs, issue context, database checks, and structured engineering prompts.
Production Ownership
Worked inside a large production codebase with focus on safe changes, clear handoff, monitoring, and operational readiness.
Fundamentals
Software engineering fundamentals I work with
These are the core engineering areas I focus on while building, debugging, and improving backend systems.
Backend Fundamentals
REST APIs, validation, authentication, authorization, pagination, and API contracts.
Database Fundamentals
SQL, joins, indexes, transactions, query optimization, caching, and SQL vs NoSQL tradeoffs.
System Design Fundamentals
Scalability, queues, retries, idempotency, rate limiting, load balancing, and fault tolerance.
Production Fundamentals
Logging, metrics, dashboards, debugging, incident response, rollbacks, and reliability checks.
Security Fundamentals
Secure coding, secrets management, JWT/OAuth, RBAC, encryption basics, and OWASP awareness.
Technologies
Core tools at a glance
A quick visual map of the tools I use most often across implementation, cloud, data, observability, and AI-assisted work.
PHP
TypeScript
Node.js
NestJS
AWS
Docker
Postman
PostgreSQL
DynamoDB
Redis
Grafana
Claude
Technology map
Tools grouped by engineering capability
A practical map of how I use tools across implementation, data, architecture, delivery, production support, and AI-assisted workflows.
Backend Development
Core languages and API patterns I use for service implementation and integration work.
PHPTypeScriptNode.jsNestJSREST APIsOpenAPI/SwaggerPostman
Databases And Data
Storage and query choices I use when balancing correctness, speed, and cost.
PostgreSQLDynamoDBRedisIndexingQuery OptimizationTransactionsData ModelingCaching
System Design
Patterns I use to reason about scale, reliability, async work, and service communication.
MicroservicesDistributed SystemsEvent-Driven ArchitectureQueuesRate LimitingIdempotencyRetry HandlingLoad BalancingHigh AvailabilityFailure Handling
Cloud And Delivery
Infrastructure and release tools I use to ship changes safely and keep services operable.
AWSIAMEC2S3SQSLambdaECS/FargateDockerCI/CDCanary Rollouts
Observability And Debugging
Signals I rely on to investigate failures, track health, and explain production behavior.
GrafanaKibanaCloudWatchRoot Cause AnalysisFive Why's RCA
AI-Assisted Engineering
AI tools and workflows I use to speed up investigation, documentation, and RCA preparation.
Claude CodeMCPPrompt Engineering
Engineering themes
Selected engineering work
High-level summaries of backend work, written generically so the engineering value is clear without exposing company-specific implementation details.
AI-Assisted Debugging Workflow
An AI-assisted debugging workflow that connects issue context, logs, database checks, and structured RCA prompts to support root-cause analysis and fix planning.
Reduced manual incident-analysis effort by making root-cause analysis more structured, repeatable, and easier for engineers to adopt.
Claude CodeMCPAzure DevOpsKibanaDatabasesPrompt Engineering
Read case studyFailure Classification And Observability
A reliability initiative that classified temporary, permanent, and customer-action-needed workflow failures while preserving retry history for clearer diagnostics.
Improved support visibility by standardizing failure categories, preserving retry history, and expanding structured diagnostic coverage.
Node.jsPHPKibanaGrafanaCloudWatch
Read case studySaaS Data Routing And Migration Safety
A backend data-routing initiative for a multi-tenant SaaS product, focused on safe storage decisions during a phased data movement workflow.
Improved data movement safety by making storage decisions explicit through phased state-machine routing and buffered update handling.
PostgreSQLNode.jsAWSDistributed SystemsState Machines
Read case studyPipeline
Future projects and learning direction
A transparent view of where I want to take this portfolio and my engineering practice next.
Now
Portfolio as an engineering knowledge base
Keeping the portfolio focused, current, and easy to extend as engineering notes and case studies are refined.
Next.jsMDXTypeScriptVercel
Next
Backend architecture diagrams library
Building visual notes that simplify backend concepts and architectures such as state machines, migrations, retry flows, observability, and distributed service patterns.
MermaidSystem DesignDocumentationBackend Architecture