AI Specialist | Production LangChain Engineer | Full-Stack Developer
Building intelligent enterprise automation at Commonwealth Bank of Australia
I'm a results-driven AI specialist with 5+ years of enterprise experience architecting production-grade automation and intelligent systems. I specialize in LangChain + AI workflows, bridging business logic with large language models to deliver transformative solutions across banking, fintech, and telecom domains.
At Commonwealth Bank of Australia, I've built end-to-end agentic systems, reduced operational cycles by 70% through intelligent automation, and led the integration of AI into core business processes.
Current Focus: Production LangChain implementations, RAG systems, AI agents, and full-stack cloud solutions.
| Category | Skills |
|---|---|
| AI & LLM | LangChain, Claude Sonnet, Prompt Engineering, RAG Systems, Agentic AI, Vector Databases, Fine-tuning |
| Backend | Python, REST APIs, FastAPI, Pega BPM, ETL/Data Pipelines, GraphQL |
| Frontend | Next.js, React, TypeScript, Tailwind CSS, DaisyUI, React Query |
| Cloud & DevOps | AWS (EC2, S3, Lambda, DynamoDB), Docker, GitHub Actions, CI/CD |
| Databases | PostgreSQL, MySQL, DynamoDB, Vector DBs (Pinecone, Weaviate) |
| Enterprise Tools | Pega 8.6, JIRA, Confluence, Streamlit, n8n |
Production Impact:
- 70% Cycle Time Reduction: Automated regression testing from 1-2 weeks → 3 days (comprehensive semi-automated test suite with progress tracking)
- 3 Production LangChain Agents: Built end-to-end agentic systems for merchant intelligence and operational automation
- Merchant MCC Prediction Engine: Agentic AI system using Claude Sonnet 4.0 to analyze business descriptions and predict merchant category codes with 95%+ accuracy
- Merchant Onboarding Testing Automation: Multi-product testing framework covering 15+ product types, merchant classifications, pricing models, and regulatory and credit checks
Banking & Fintech Expertise:
- Merchant onboarding workflows
- Payment processing automation
- Regulatory compliance integration
- High-volume transaction handling
Tech Stack: Python, Streamlit, REST APIs
Impact: Reduced testing cycle from 1-2 weeks to 3 days
Comprehensive regression testing framework for merchant onboarding supporting:
- Single & bulk test case creation
- 15+ onboarding product types
- Merchant type classification, pricing models, address validation
- Nature of business categorization, credit checks
- REST API integration for CI/CD automation
- Export reports for progress tracking
Why it matters: This automation saved the team 200+ hours quarterly while maintaining test coverage.
Tech Stack: Python, LangChain, Claude Sonnet 4.0, REST APIs
Status: In Production
End-to-end agentic AI system that:
- Analyzes merchant business descriptions using Claude Sonnet 4.0
- Predicts relevant Merchant Category Codes (MCC) with semantic understanding
- Validates MCCs via REST API calls to ensure regulatory compliance
- Extracts business tokens and cross-references against valid MCC database
- Determines accurate merchant nature of business classification
Impact: Eliminates manual MCC assignment (~4 hours/week), improves accuracy, enables intelligent merchant categorization for risk assessment.
Tech Stack: Next.js, React, PostgreSQL, Tailwind CSS, TypeScript
Status: Production Deployed
Full-stack SaaS application featuring:
- Authentication: Secure login/session management
- Search & Discovery: Filter by location, amenities, cost, rooms, property type
- Shopping Cart: Save interested properties
- Messaging: In-app messaging & SMS notifications to landlords
- Backend: PostgreSQL for application logs, case management, session tracking
- Real-time Updates: React Query for optimized data fetching
Why featured: Demonstrates full-stack competency combining AI-ready architecture (REST-first, modular backend) with modern React patterns.
| Certification | Status |
|---|---|
| AWS Cloud Practitioner | ✅ Completed |
| AWS Solutions Architect Associate | 🔄 In Progress |
| Pega Certified Developer (CPDC) | ✅ Completed |
| Python Programming | ✅ Completed |
| React JS | ✅ Completed |
✨ Production LangChain Expertise: 3+ agentic systems deployed in banking environment
✨ AI + Enterprise BPM: Rare blend of Pega mastery + modern AI stack
✨ Tier-1 Bank Experience: CBA-scale transaction handling, regulatory compliance, mission-critical systems
✨ Full-Stack AI: Python backends → Next.js frontends → Cloud deployment
✨ Operational Impact: 70% automation gains, measurable business outcomes
🤖 AI/ML & LLM
├── LangChain (agents, chains, memory)
├── Claude Sonnet 4.0, GPT-4
├── RAG & Vector DBs
├── Prompt Engineering
└── Model Fine-tuning
🐍 Python & Backend
├── FastAPI, Flask
├── REST APIs & AsyncIO
├── Data Pipelines & ETL
└── Streamlit (rapid prototyping)
🎨 Frontend & Web
├── Next.js 14+ (SSR, ISR, App Router)
├── React 18+, TypeScript
├── Tailwind CSS, DaisyUI
├── React Query, React Hook Form
└── TailwindCSS animations
☁️ Cloud & DevOps
├── AWS (EC2, S3, Lambda, RDS, DynamoDB)
├── Docker & Docker Compose
├── GitHub Actions CI/CD
└── Infrastructure as Code
🏢 Enterprise
├── Pega 8.6 (BPM, CDH, Marketing Automation)
├── JIRA, Confluence, GitHub
└── GraphQL, SQL, NoSQL
🗄️ Databases
├── PostgreSQL, MySQL
├── DynamoDB
└── Vector Databases (Pinecone, Weaviate)
- 🚀 Roles: AI Specialist, Senior Backend Engineer, AI/ML Engineer, Tech Lead
- 🌍 Locations: International (US/Europe) + India-based remote
- 🏢 Companies: FAANG, Fast-growing AI startups, Fintech innovators
- 💡 Challenges: RAG systems at scale, fine-tuning LLMs, agentic workflows, cloud architecture
I'm open to:
- Recruiting conversations for AI specialist, senior engineer, or tech leadership roles
- Technical discussions about LangChain, RAG, production AI systems
- Collaboration on open-source AI projects
- Mentorship on Python, LangChain, or cloud architecture
Reach out:
"The best automation is intelligent automation. Build systems that learn, adapt, and improve—not just execute."
Last updated: Jan 2026 | Always learning, always building