Software Engineer · Agentic AI Systems
Systems that reason,
not just execute.
I'm Kshitiz Raj, a software engineer at Barclays. I build full-stack platforms and event-driven backends by day, and multi-agent, reasoning-first systems on the side — the kind that plan, delegate, and validate rather than run a fixed script.
Two years, one bank, a shift from forecasting risk to running infrastructure.
Both roles at Barclays, in Pune — first building the ML that flags project risk, now building the systems that run in production.
Mar 2025 — Current
Software Engineer, BA4
Barclays · Pune, IN
Own delivery across CI/CD, GraphQL APIs, and reporting infrastructure for systems running in production on Red Hat OpenShift.
- ~10 reposMigrated CI/CD pipelines to AWS using CloudFormation and Service Catalog, moving the team off Bitbucket Stash onto GitLab with reusable pipeline components.
- GraphQLBuilt and maintained Node.js GraphQL APIs for data aggregation, giving client teams flexible, on-demand access to shared data.
- JWTImplemented authentication and authorization with JWT tokens, enforcing access control and session integrity across application layers.
- SQL ServerDesigned and optimized complex queries and stored procedures for high-volume reporting workflows.
- OpenShiftDeployed and operated UI and API services across multiple Red Hat OpenShift clusters for scalability and consistency.
Jul 2024 — Feb 2025
Technical Graduate Developer, BA3
Barclays · Pune, IN
Built the ML systems that forecast project health, taking a manual RAG-status process into a model teams could trust.
- 82% → 96%Built and deployed XGBoost and LightGBM models to forecast project RAG (Red/Amber/Green) status, lifting accuracy from 82% to 96% at 91% precision — cutting false-positive risk alerts.
- End-to-endRan full data science workflows — EDA, feature engineering, hypothesis testing, statistical validation — across historical, time-series, and categorical data to isolate real risk indicators.
- 4h → 1.5hCut ML training time 63% through efficient feature pipelines, model tuning, and stratified k-fold cross-validation.
Five layers of the same stack, from reasoning down to reliability.
I don't think of AI work and infrastructure work as separate disciplines — the same instinct for clear boundaries and predictable failure applies to both.
Agentic AI Systems
01Multi-agent architectures with a centralized root agent for planning, delegation, and session-level context — RAG retrieval paired with explicit validation stages, not just chained prompts.
Backend & Distributed Systems
02Event-driven services that hold up under real load — Kafka pipelines, async task execution, and APIs built to be consumed by more than one kind of client.
Data & Applied ML
03Models built to be trusted in production, not just accurate in a notebook — feature pipelines, validation, and tuning aimed at cutting false positives, not chasing a leaderboard score.
Cloud & Reliability
04Infrastructure that a team can operate, not just Claude can deploy once — CI/CD as reusable components, multi-cluster deployments, consistent environments.
Frontend
05Interfaces that stay out of the way — fast, mobile-first, and built to match the reliability of the backend behind them.
What I build when nobody's asking me to.
Side projects that push into territory the day job doesn't — governed multi-agent systems, event-driven platforms, and a few things built purely out of curiosity.
I build systems the way I'd want to inherit them.
Whether it's an agent pipeline or a reporting service, the sequence is the same — because the failure modes are the same.
Map the flow
Before writing code, trace how data and requests actually move through the system — not the version on the architecture diagram.
Design the pipeline
Draw clear boundaries between reasoning, execution, and context — so the system stays auditable as it grows, not just fast to demo.
Ship and harden
Deploy with CI/CD, monitor what's running, and iterate against real usage — reliability is a feature, not an afterthought.
The toolkit, laid flat.
Languages
Applied AI
Backend & Systems
Data & Storage
Cloud & DevOps
Frontend
ML & NLP
Foundations.
2020 — 2024
SRM Institute of Science & Technology
B.Tech, Computer Science & Engineering
Chennai, TN · CGPA: 9.05 / 10
2017 — 2020
Delhi Public School
AISSCE (12th) · AISSE (10th)
Bokaro Steel City, JH · 90.20% · 88.4%
Certifications & Courses
- Barclays — Agentic AI Digital Credential
- Barclays — Gen AI Digital Credential
- Google — Build Intelligent Agents with ADK
- Google — Agents Intensive Capstone Project
- 100xDevs — Full Stack Development (0–1 & 1–100)
- NPTEL — Data Science for Engineers
- Red Hat — Enterprise Linux Fundamentals
VII · Let's Talk
Building something that needs to reason, not just run?
Open to full-stack and agentic-AI engineering conversations. The fastest way to reach me is email.