Wealth Management Platform 09 / 2025 — 01 / 2026 · Senior Software Developer · Leading banking client
Worked on an in-house wealth management system for a leading banking client. The platform supported mutual fund transactions, investment portfolio management and customer reporting.
- Developed workflows to support buying and selling mutual funds.
- Processed reverse-feed files and used the data to update transaction and portfolio information.
- Built portfolio reports, customer statements and other investment-related documents.
- Developed backend services using Java and integrated services through Kafka.
- Worked with MongoDB for portfolio, transaction and reporting data.
- Supported workflow orchestration and scheduled processing using Airflow.
- Worked with Kubernetes-based deployments for containerised application services.
- Used Claude as an AI-assisted development tool to accelerate implementation and support rapid application development.
Antibago 02 / 2026 — 04 / 2026 · ML Engineer · Leading healthcare client
Worked as an ML Engineer on an image-classification solution for a healthcare and laboratory platform.
- Migrated image-classification workflows from TensorBoard to Vertex AI.
- Supported the transition of machine-learning experiments and model workflows to a managed cloud platform.
- Supported MLOps activities by helping build pipelines for machine-learning workflows and model delivery.
- Worked with ML data preparation and AI-powered solutions for business users.
- Supported IVDR audit activities related to the ML platform and project delivery.
DNA Platform Solution Enablement 05 / 2026 — Present · AI Engineer · Leading biopharmaceutical client
Working as an AI Engineer on a platform that enables business lines of business to create workspaces and AI agents based on their requirements.
- Building an agent platform that helps business teams create and use solutions for different business requirements.
- Developing specialised AI agents using Amazon Bedrock and LangChain.
- Working with ReAct agents, tool calling and Model Context Protocol (MCP) to connect agents with tools and external capabilities.
- Designing Retrieval-Augmented Generation (RAG) agents to work with business knowledge and provide context-aware responses.
- Supporting reusable patterns for agent creation, workspace enablement and AI solution development.

