During my PhD, I was passionately engaged in the field of medical data mining. My primary focus lies in developing advanced models for segmenting pseudo-periodic datasets and conducting anomaly detection. My aim was to push the boundaries of traditional algorithms, seeking superior speed, accuracy, and performance compared to state-of-the-art deep learning models. Despite my dedication, I made the difficult decision to prioritize my family commitments, leading to a graceful withdrawal from the PhD program.𝐒𝐭𝐞𝐥𝐥𝐚𝐫 𝐏𝐫𝐨𝐣𝐞𝐜𝐭:Leading a groundbreaking project involving the development of a state-of-the-art algorithm designed to segment medical data for superior anomaly detection. Currently undergoing rigorous review for publication in a respected journal.𝐊𝐞𝐲 𝐀𝐜𝐜𝐨𝐦𝐩𝐥𝐢𝐬𝐡𝐦𝐞𝐧𝐭𝐬:● Developed and implemented an advanced anomaly detection solution for time series data, leveraging both traditional statistical methods and cutting-edge deep learning algorithms.● Integrated state-of-the-art traditional techniques into the anomaly detection framework, resulting in a more robust and accurate solution for identifying irregularities in time series data.● Collaborated with a diverse team to translate complex anomaly detection algorithms into user-friendly features for seamless system integration.● Conducted extensive market research to identify industry-specific needs for anomaly detection, contributing valuable insights that shaped the product's roadmap and alignment with market demands.● Utilized data analytics and insights to continuously refine and optimize the anomaly detection model, ensuring high precision and adaptability to dynamic data patterns.● Led the anomaly detection project through all phases, from conceptualization to implementation.● Successfully conveyed complex tech concepts to non-tech stakeholders, fostering shared understanding and gaining support for anomaly detection initiative.

𝐒𝐭𝐞𝐥𝐥𝐚𝐫 𝐏𝐫𝐨𝐣𝐞𝐜𝐭:Championed initiatives for superior technographics data quantity, securing wins in "Bake-Off" for revenue growth by on-boarding new data sources and advanced AI algorithms. With a sub-goal to expand the install base and facilitate European market targeting for regional expansion.𝐊𝐞𝐲 𝐀𝐜𝐜𝐨𝐦𝐩𝐥𝐢𝐬𝐡𝐦𝐞𝐧𝐭𝐬:● Implementation of Advanced Technographics Tracking: Successfully led the implementation of advanced technographics tracking within the Demandbase platform, enhancing the precision and depth of data analysis.● Cross-Functional Collaboration: Facilitated seamless collaboration between the product development team and sales teams, resulting in a 20% increase in technographic-related feature adoption.● User-Centric Feature Enhancements: Spearheaded the development of user-centric features based on technographics insights, resulting in an improvement in user satisfaction scores.● Data Accuracy Improvement: Implemented robust data validation processes, leading to a 30% reduction in inaccuracies within the technographic data, thereby increasing overall data reliability.● Product Roadmap Alignment: Aligned the product roadmap with evolving technographics trends, ensuring the continuous relevance of the platform and addressing emerging market demands effectively.● Market Expansion Strategies: Contributed to the development of market expansion strategies by leveraging technographics data, leading to successful product launches in new sectors.● Competitor Benchmarking: Developed competitor benchmarking with technographics, informing product strategies, boosting market share.● Development Implementation: Successfully transitioned the team to an agile development methodology, resulting in a 20% increase in the speed of delivering technographics-related features and improvements.● Continuous Learning Initiatives: Implemented continuous training for team's tech awareness, fostering innovation.

𝐊𝐞𝐲 𝐀𝐜𝐜𝐨𝐦𝐩𝐥𝐢𝐬𝐡𝐦𝐞𝐧𝐭𝐬:● Building project plans to further optimize the existing delivery cycle.● Facilitated Early Adoption Program (EAP) programs for two Fortune 500 companies belonging to orthogonal verticals. ● Mentored a cross-functional team of 10 resources to deliver quality products.● Lead 2 product teams transition to Agile product development.● Started up regular scrum practices and launched 3 delivery ready products.● Utilized Agile scrum practices to help the team increase their velocity within few months of adoption by 60%.

𝐒𝐭𝐞𝐥𝐥𝐚𝐫 𝐏𝐫𝐨𝐣𝐞𝐜𝐭:Led initiative to create ETL data pipelines, reducing costs and enhancing efficiency significantly.𝐊𝐞𝐲 𝐀𝐜𝐜𝐨𝐦𝐩𝐥𝐢𝐬𝐡𝐦𝐞𝐧𝐭𝐬 𝐚𝐬 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫:● Prototyping and building ETL data pipelines across multiple cloud infrastructure----> reduce the cloud cost by 80%----> reduced output data generation from weeks to hours.----> reduced the legacy code by 99.8%----> reduced the number of core languages used to write code to 1 i.e. PySpark● Developed the reasoning based architecture for an SDK to run multiple data pipelines using a dispatch model with the core engineering team.● Converted data science team Python code to PySpark to finish output generation 8x faster.𝐊𝐞𝐲 𝐀𝐜𝐜𝐨𝐦𝐩𝐥𝐢𝐬𝐡𝐦𝐞𝐧𝐭𝐬 𝐚𝐬 𝐒𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 (𝐅𝐮𝐥𝐥 𝐒𝐭𝐚𝐜𝐤 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫):● Frontend----> Used Elastic Search to improve query performance from minutes to milliseconds----> Reduced the initial page load time of the application from 10 seconds to 3 seconds using different performance optimization techniques.----> Designed and implemented the AWS architecture (including VPC, ELB, Cloudwatch, ECS & Fargate)----> Improved/extended existing internal/partner web applications using technologies like ReactJS, Redux, Javascript/ES6, ES, MongoDB/Cloud SQL.● Backend----> Using Postman and for testing and verifying the REST APIS.----> Implemented Domain-Driven Design, CQRS, Event-Driven and Hexagonal Architecture design patterns.----> Application development based on 12-Factor Principles and following other best practices.----> Developed fully automated CI/CD solution using Bitbucket pipeline and deploying to AWS Fargate.----> Designing & development of REST-based Cloud-Native Multi-tier Microservices applications from scratch using Java, MongoDB, Spring Boot, AWS/GCP.● Database----> Designed improved schema based on integer-based search then string which reduce storage and made joins 30X faster.

During my employment with Arcitecta, I explored and worked on different type of cloud platform including Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP). As a part of Arcitecta team, I played my part towards enhancing the power of Mediaflux to move petabytes of data every month for our customers irrespective of any cloud platform by providing single virtualised storage layer to them.𝐒𝐭𝐞𝐥𝐥𝐚𝐫 𝐏𝐫𝐨𝐣𝐞𝐜𝐭:DigitalGlobe has been collecting imagery of Earth from space since its first satellite launched in 2001.Over the years, DigitalGlobe has built a world-class constellation, with increased collection capacityand resolution. With more than 7 billion square kilometres of images, DigitalGlobe’s archive at thattime totals 100 petabytes of storage, increasing by around 10 PB per year. I was tasked to work withAmazon to transfer all this data from the existing tape library to the Cloud – set to be Amazon’sbiggest data repository from a single client.𝐊𝐞𝐲 𝐀𝐜𝐜𝐨𝐦𝐩𝐥𝐢𝐬𝐡𝐦𝐞𝐧𝐭𝐬:● Engineered a Java plugin optimizing data transfers to AZURE, AWS, and Google Cloud, resulting in a 30% reduction in transfer times and enhanced scalability.● Spearheaded health data analytics initiatives for prominent hospitals, such as the Royal Melbourne Hospital, achieving a 20% improvement in data cleaning and processing efficiency and insightful analysis of questionnaire data.● Led the end-to-end design, development, and planning for the seamless incorporation of My Health Record, improving data accessibility and ensuring compliance with healthcare regulations.Directed maintenance and implementation efforts for new features in data management platforms Mediaflux and CAReHR (for healthcare data), resulting in a 15% increase in platform efficiency and an enhanced user experience.

𝐒𝐭𝐞𝐥𝐥𝐚𝐫 𝐏𝐫𝐨𝐣𝐞𝐜𝐭:Built a robust, fast and scalable web application to combine all the energy research group's workin a single unified offering, that could serve both as a tool for the researchers, and as a platform toshowcase their research work in solar, storage, and buildings in an intuitive way to otherresearchers and clients.𝐊𝐞𝐲 𝐀𝐜𝐜𝐨𝐦𝐩𝐥𝐢𝐬𝐡𝐦𝐞𝐧𝐭𝐬:● Drive solutions and services for key front-office and back-office enterprise processes (CRM, ERP, HCM) by leveraging best-of-breed SaaS solution stacks such as Salesforce, NetSuite, Workday and ecosystem partners.● Deployment of application build and managed on the GCP platform to IBM Cloud .● Engineer solutions on GCP foundation platform using Infrastructure As Code methods● Integrate, configure, deploy and manage centrally provided common cloud services (e.g. IAM, networking, logging, Operating systems, Containers.)● Ensure compliance with Security● Build and support CI / CD and continuous testing activities● Engineering activities to implement patches provided centrally

Mentoring program helped me to learn and grow, not only technically but from a consulting perspective. I followed the agile practices, technical mentorship to efficiently learn how to develop end-to-end solution. This knowledge encouraged to explore about:- Virtualization: Docker- Cloud/PaaS/IaaS: Microsoft Azure, Kubernetes, Docker Swarm, CloudFoundry- Infrastructure provisioning/testing: Terraform- Programming languages: Shell scripting- Continuous Delivery: Jenkins- Web servers/Application servers: Apache, Tomcat- Databases: MySQL, Elasticsearch, MongoDBVersion control: Git, SVN

·     Received Hands-on experience with DynamoDB, Lambda, S3, SNS, SQS, Step Functions, CloudWatch, CloudTrail, IAM.·     Gained strong practical experience with designing infrastructure for RESTful services.·     Gained experience about designing, installing, configuring complex integrations across different cloud services and regions·     Gained experience with ‘infrastructure as code’, e.g. SAM, Cloud Formation·     Exposure to DevOps and Agile.·     Gained experience with Jenkins and GitHub·     Maintained and configured CI/CD methodology.

Regular public speaker in different Meetup groups like Melbourne Developer Meetup (Melbourne) about how GCP and Azure have the advantage of building and maintaining a vast backbone network, however, AWS the current market leader, focused initially on quick delivery of services to the marketplace, rather than building out a vast backbone network. And why performance differences in certain geographies emphasises the truth that public cloud vendors do not yet have stable performance worldwide.