// Pune, India · 11+ years

Ravi Baghel

Senior Full Stack Engineer · AI Forward Deployed Engineering

I embed with business teams in regulated domains and ship production systems end-to-end — Java 21 / Spring Boot, Angular, Kafka, Oracle and cloud. At BNY I own a core banking platform and have spent the last year wiring AI into how it is built and operated: custom MCP servers, agentic delivery workflows and human-in-the-loop guardrails. I also build FitFlexFlow, an independent SaaS platform used as a proving ground for the same practices.

11+

Years experience — banking, fintech, IoT, SaaS

8+

MCP servers built — logs, databases, ticketing, source control, docs, artifacts, E2E

Ticket → MR

Agentic delivery workflow with human approval gates

0 → prod

SaaS platform built & operated solo (FitFlexFlow)

About Myself

Hi! I'm Ravi, a hands-on engineer who has spent 11 years embedded with client and business teams — at BNY, IBM, Cognizant and Harman, delivering for Barclays and Bajaj Finance — owning the full lifecycle from discovery and architecture through build, release and production support. Most of that work has been in regulated, high-availability domains where I had to reverse-engineer undocumented systems, translate ambiguous business requirements into working software, and be accountable for what ran in production.

My foundation is Java / Spring Boot, Angular, Kafka and Oracle on Docker, OCI and AWS, with security models built on OAuth2, JWT and RBAC. Over the last year I have applied that foundation to AI engineering: designing MCP servers that expose enterprise systems — logs, databases, tickets, repositories, artifact stores — as typed, role-gated tools for AI agents; building agentic workflows that take a Jira ticket to a reviewable merge request or a vulnerability to a patched image; and putting approval gates, least-privilege access and auditability around every autonomous step.

FitFlexFlow, my independent SaaS platform, is where I prove these practices end to end — CI/CD, cloud, payments, video meetings and AI-native operations tooling. I'm now targeting an AI Forward Deployed Engineer role: sitting with customers, understanding their systems and constraints, and shipping AI capabilities that integrate safely with the tools and data they already run on.

Quick facts

LocationPune, India
Experience11+ years
CurrentSenior Associate, BNY
Target roleSenior AI Forward Deployed Engineer
Open toAI FDE · Solutions Engineering · Pune / Remote / Hybrid
Notice period60 days
LanguagesEnglish (Professional) · Hindi (Native)
InterestsWriting · Photography · Travel · Cricket · Badminton

Background

Technologies I have used in production, grouped by how I think about a system.

Architecture Principles

How I design systems — the trade-offs I optimise for, and the business outcomes they protect.

Design for change

Modular boundaries, dependency inversion and feature-based packaging let parts evolve independently — composition over deep inheritance. New requirements land as new modules, not risky rewrites, keeping change cheap and releases predictable.

Fail fast, degrade gracefully

Validate at startup (DB connectivity, config), fail loudly on misconfiguration, and degrade non-critical paths — email, meetings, notifications — without taking the core down. Misconfig surfaces in minutes, not in production incidents; partial outages never become full outages.

Secure by default

Least privilege everywhere — separate schema-owner vs runtime DB accounts, RBAC, secrets externalised per environment, re-authentication for sensitive admin operations. Security is a design input, not a retrofit — satisfying audit and compliance expectations in regulated domains.

Config over code

Runtime-configurable integrations — SMTP, payment gateways, meeting providers — via admin UI and application events. Behaviour changes without redeploys, so operations can respond to business needs in minutes instead of release cycles.

Observability first

Structured logs with trace/span correlation, health endpoints, Grafana dashboards and Splunk alerting designed in, not bolted on. Issues are detected before customers report them, and root cause is a query away — not a war room.

Simplest thing that scales

Start with a well-modularised monolith; extract microservices only where scaling or team boundaries demand it. Avoiding premature distribution keeps infrastructure cost, cognitive load and time-to-market low while preserving a clear path to scale.

AI ENGINEERING · FORWARD DEPLOYED

Deploying AI agents into a regulated enterprise toolchain

In a regulated enterprise you cannot hand an LLM a shell and a database password. My work over the last year has been the forward-deployed problem in miniature: take the systems a team already runs on — observability, databases, ticketing, source control, knowledge bases, artifact stores — and expose them to AI agents as typed, least-privilege tools over the Model Context Protocol, then compose those tools into agentic workflows that carry real engineering work end to end with a human approving every consequential step. The result is measurable: production triage that took an hour of log-diving is a single prompt; vulnerability patching and sprint tickets become reviewable merge requests rather than manual toil.

Tool design & MCP servers

Built 8+ MCP servers in Node.js/TypeScript on the official SDK, each exposing a small set of tools with strict JSON-Schema inputs and structured outputs designed for LLM consumption — e.g. a log-analysis tool that returns incidents with trace/span IDs, severity and logger instead of raw text, and a database tool limited to schema inspection and validated queries. Every tool is scoped to what a workflow actually needs — nothing more.

Agentic workflows with approval gates

Authored reusable IDE workflows and skills that chain tools into multi-step agents: Ticket-to-MR reads the sprint board, prioritises, produces a design and impact analysis, and only after explicit approval branches, implements, runs tests and raises an MR. Auto Vulnerability Fix and Image Scan Fix pull CVE tickets, resolve safe dependency or base-image upgrades, and open reviewable changes. Every autonomous action is bounded, logged and reversible.

Guardrails for regulated environments

Security is designed into the tool layer, not the prompt: read-only defaults, statement-type validation before any query runs, environment selection as an explicit typed argument, and no credentials stored in the servers. Database access is confined to lower environments — read-only by default, with a limited set of role-gated write operations permitted only up to QA; production data is never writable through an agent. Destructive operations are refused outright — the agent's blast radius is bounded by the tool contract, not by model behaviour.

Adoption, context & cost

Drove team adoption by packaging workflows as slash commands with documented prerequisites and failure modes. Persistent memory and scoped tool responses keep context windows small — lower token spend, higher answer accuracy. Playwright-backed E2E and regression-test generation give the agent a verification loop, and code review assistance keeps AI-authored changes to the same standard as human ones.

Project Highlights

INDEPENDENT LEARNING PROJECT · 2025 – PRESENT

FitFlexFlow Platform

A self-driven learning project engineered like a commercial product: a multi-tenant SaaS for online/offline fitness classes covering subscription lifecycle, country-aware pricing and currency formatting, payment-gateway abstraction (Razorpay, PayPal) with webhook-driven confirmation, live Zoom/Google Meet classes, and a full admin console. Technically it is a modular Spring Boot 3 / Java 21 monolith serving an Angular 18 SSR frontend, secured with OAuth2 + JWT + RBAC, persisted on Oracle Autonomous DB over mutual TLS, and shipped through a Docker + Nginx + GitHub Actions pipeline on Oracle Cloud — demonstrating end-to-end ownership from data model to production operations.

Account Opening & Maintenance · BNY

Core banking platform where SIS records are created and maintained for account opening and maintenance in a regulated, high-availability environment. I own full stack delivery and solution architecture: Java / Spring Boot services with an Angular UI. Led the migration of undocumented legacy applications by reverse-engineering business rules and re-platforming them onto a modern Spring Boot target architecture. Designed a scheduler + watcher based data-collection framework that ingests heterogeneous sources — files, REST APIs, AWS S3 and databases — into Oracle and Sybase. Built event-driven integrations on Kafka and MQ: consuming account events from multiple primary and secondary clusters with feature-based listeners that deduplicate messages and route them to different downstreams, and producing/consuming SIS data over MQ to keep the UI in sync with back-office changes in near real time.

HUE — Hierarchical User Entitlement · Barclays

Enterprise identity-and-access-management platform for Barclays where resources are organised hierarchically (Org → Project → Resource). I designed the policy-inheritance model so IAM policies defined at any node automatically propagate to all descendants — eliminating duplicated entitlement configuration and reducing access-provisioning errors across thousands of resources. Delivered as Spring microservices with an Angular front end, the platform gave business and security teams a single, consistent way to grant, audit and revoke access, cutting onboarding time for new projects while strengthening the bank's least-privilege and compliance posture.

Loan Management System · Bajaj Finance

Real-time digital lending platform for Bajaj Finance built on AWS with Java microservices, Oracle DB and an Angular front end. The system orchestrates the full disbursement pipeline — application validation, credit checks, approval and payout — so approved loans reach the customer's bank account in under 3 minutes, a key business differentiator for instant digital lending. I owned module design, implementation and release, working within a distributed microservice landscape where reliability, low latency and fault isolation were critical to sustaining high application volumes and a seamless customer experience.

IoT Gateway · Harman

Edge-to-cloud IoT gateway platform at Harman connecting sensors, IoT modules and smart devices to cloud services over MQTT. The gateway acts as a wireless access portal — providing internet connectivity, protocol translation and secure data routing for heterogeneous edge devices that could not reach the cloud directly. I implemented requirements and owned modules across the device, gateway and cloud layers, and debugged field issues end to end. The platform enabled customers to onboard and manage connected devices at scale, turning fragmented edge hardware into a centrally manageable, cloud-integrated fleet.

Work History

My Projects

Selected work, with the architectural decisions that mattered.

INDEPENDENT LEARNING PROJECT · FULL STACK · 2025 – PRESENT

FitFlexFlow Platform rebuild.fitflexflow.com ↗

Java 21Spring Boot 3.3Angular 18 SSROracle ADBOCIDocker · NginxGitHub Actions

End-to-end SaaS for online/offline fitness classes — subscription lifecycle, payments, live meetings and admin operations — designed and built solo as a learning project, but engineered to commercial standards: every concern from schema design and security to CI/CD and production operations is owned, automated and observable.

Architecture

A deliberately modular monolith: a single deployable Spring Boot app serving an Angular SSR frontend, with layered controller / service / repository / core design and feature-based packaging so modules can be extracted into microservices if the business scales. Configuration is modular per concern (persistence, security, payments, messaging, TLS, monitoring) with externalised, environment-specific secrets (dev / QA / prod) — keeping the codebase simple to operate while staying ready to scale.

Security

Secure-by-default design: Spring Security with OAuth2 client + resource server, JWT token issuance, and role-based access strategies (Admin / Instructor / Student) enforced via security filters. CORS is hardened, sensitive settings changes require admin re-authentication, and a dynamic maintenance mode — a filter-driven maintenance window with a user-facing notification page — can be toggled at runtime without redeploys, protecting both users and revenue during planned downtime.

Data

Oracle Autonomous DB over mutual TLS with a least-privilege model separating schema ownership from the runtime application account — so a compromised app credential cannot alter structure. The connection pool is tuned with fail-fast startup and leak detection for resilience under load, and versioned SQL migration scripts keep schema changes auditable and repeatable across environments.

Integrations

A pluggable payment-gateway abstraction (Razorpay, PayPal) with webhook-driven confirmation keeps revenue flows provider-agnostic. Social login (Google, Facebook) via OAuth2 reduces signup friction; Zoom Web SDK & Google Meet lifecycle management powers live classes; Google / Microsoft Graph handles calendar and email; and runtime-configurable SMTP (admin UI → application event → hot-reloaded mail sender with connection test before persist) lets operations change email providers without a redeploy.

Cloud & DevOps

Dockerised on OCI Compute behind Nginx (TLS termination, per-environment location configs, maintenance page) for a portable, reproducible runtime. A GitHub Actions deploy pipeline with traceable build signatures gives auditable releases, and scripted build-restart and secrets rollout keep deployments fast and consistent — the same operational discipline expected of a production SaaS.

Frontend performance

Performance treated as a feature: Angular SSR + prerendering for fast first paint and SEO, lazy-loaded feature routes and standalone components for smaller bundles, OnPush-style change detection to minimise render cycles, and asset caching with CDN-friendly static serving behind Nginx — keeping the experience fast for users on any device or network.

AI-native operations

The platform is operated through its own MCP tool layer: a logs server that pulls container logs and parses ERROR/WARN/FATAL lines into incidents with trace/span IDs, filtered by time window; a database server that is read-only in lower environments with a limited set of role-gated write operations permitted only up to QA — production data is never writable through an agent; and a Playwright server for E2E verification. AI assistants triage issues, inspect data and verify fixes in minutes — within the same least-privilege boundaries a human operator would have.

Education

2014 – 2015 · Pune

PG Diploma — Advanced Computing, Wireless & Mobile Communication (PG-WiMC)

Sunbeam Institute of Information Technology (CDAC)

2009 – 2013 · Meerut

B.Tech — Information Technology

Uttar Pradesh Technical University, Lucknow

Let's build something solid.

Open to AI Forward Deployed Engineer and solutions engineering roles. Happy to talk MCP tool design, agentic workflows in regulated environments, or integrating AI into existing enterprise platforms.