Jira AI Task Generator
LiveTransforms rough requirements into Jira-ready tickets with acceptance criteria and subtasks, helping teams plan and ship faster.
I build production-ready AI systems, scalable backend architectures, and developer tools that solve real-world problems and handle real users.
I’m a Software Developer with ~3 years of experience building real-world systems used by thousands of users. I specialize in backend engineering, AI-powered products, and scalable APIs.
At Careers360, I’ve worked on production-grade systems including APIs, internal tools, and AI-driven features that improved user engagement and performance. I focus on writing clean, scalable, and maintainable code.
My core strength lies in combining AI with practical engineering — building tools like automated Jira generators, job analysis systems, and developer productivity tools.
Currently, I’m focused on building intelligent systems and preparing for high-impact engineering roles where I can contribute to large-scale products.
Build intelligent AI agents that can autonomously perform complex tasks, make decisions, and interact with multiple systems to solve real-world problems.
Production-ready AI agents, automation tools, and GPT-powered systems for startups and businesses.
Develop scalable REST APIs, microservices, and web services that handle high traffic and integrate seamlessly with existing systems.
High-performance REST APIs, backend systems, and scalable microservices with clean architecture.
Create complete web applications from frontend to backend, including database design, user authentication, and deployment strategies.
Fast, modern web applications with strong backend + clean UI (React / FastAPI / DB).
Official extension products and tools built for real-world developer and productivity workflows.
Transforms rough requirements into Jira-ready tickets with acceptance criteria and subtasks, helping teams plan and ship faster.
Provides function-level history, ownership, and evolution insights so developers can debug and onboard faster.
AI-powered system that converts raw product requirements into structured Jira tickets with acceptance criteria, subtasks, and engineering breakdowns — reducing planning time by 60%.
Job Lens AI (Job Analyzer) for job hunting that analyzes job descriptions, identifies skill gaps, and helps plan focused interview preparation.
Code Archeology for VS Code to get all details of function, including history, ownership, and evolution for faster debugging.
Careers360
2021 - Present (3 years)
Continuous Learning
2019 - 2023
A practical breakdown of the architecture, guardrails, and deployment choices I use to ship reliable AI agents in production.
The core API design decisions that prevent breaking changes, keep latency stable, and support fast product iteration.
A hands-on guide to indexing, query shaping, and monitoring patterns that consistently reduce DB bottlenecks.
Focus on the DevOps habits that improve reliability quickly without overcomplicating your stack.
A simple blueprint for designing Kafka topics, consumers, and retries that stay stable under real traffic.
Deep dives from the latest articles
Most AI agent systems fail in production because they optimize for demo quality, not reliability. My baseline architecture keeps one orchestrator service, task-specific tools, strict output contracts, and clear retry limits. The objective is to make the agent predictable before making it fancy.
I start by defining deterministic tool boundaries: each tool does one thing, validates input, and returns machine-friendly output. Then I wrap tool calls with timeout policies, idempotency keys, and observability hooks. This gives us clean traces for every failed run and makes debugging practical for real teams.
The biggest win usually comes from guardrails: schema-based response validation, fallback prompts, and human handoff for uncertain decisions. This approach shipped faster and cut noisy failures significantly compared to complex autonomous flows.
Scalable APIs are more about contracts than code. I treat every endpoint as a long-term interface and design for backward compatibility from day one. Versioning, explicit pagination, and stable response envelopes prevent most production regressions.
Performance comes from predictable patterns: efficient database access, bounded payloads, and caching near read-heavy endpoints. I also enforce request validation and rate limits consistently so one noisy client cannot degrade the entire service.
When teams iterate quickly, change management matters. Deprecation windows, structured API changelogs, and smoke tests on critical endpoints let us move fast without breaking consumers.
The first rule of DB optimization is measuring before changing. I profile slow queries, inspect query plans, and rank bottlenecks by business impact. This avoids spending time on micro-optimizations that do not affect user experience.
High-impact fixes are usually straightforward: compound indexes for real access paths, removing N+1 query patterns, and reducing oversized joins. In write-heavy systems, batching and async processing also reduce lock pressure dramatically.
Long-term performance comes from ongoing monitoring. I track p95 latency, row scan counts, and cache hit ratios in dashboards so regressions are visible before they hit users.
You do not need a complex DevOps stack to improve delivery quality. The highest ROI practices are CI checks on every PR, reproducible environments, and safe deployments with rollback plans.
I prioritize observability early: structured logs, basic metrics, and alerting for error spikes. Once failures are visible, teams can fix systemic issues faster and reduce firefighting.
For growing products, progressive rollout and feature flags become essential. They let us ship faster while keeping risk controlled, especially when new features touch critical user flows.
Kafka works best when topic design reflects domain events clearly. I use stable event schemas, explicit partition keys, and consumer groups aligned to business functions rather than random services.
Reliability comes from operational discipline: retry topics, dead-letter queues, idempotent consumers, and offset monitoring. These patterns handle transient failures without duplicating critical actions.
For scale, I monitor lag, throughput, and processing time per consumer group. This makes it easy to spot bottlenecks and scale the right part of the pipeline before users notice latency.
Let's discuss your next project
I’m open to opportunities, freelance work, and interesting projects. If you’re building something impactful or need help with backend systems or AI — let’s connect.
karantomar207@gmail.com
Phone
+91 9621930201
Location
Gurugram, India