Ai Solution Business Intelligence

Professional Experience:

Experienced AI Solution Architect and expertise in designing and implementing scalable data architectures for enterprise-level organizations.

Leading design and delivery of a unified AI Solution data platform, including data ingestion, modeling, quality controls, and governance.

Driving business intelligence and self-serve analytics adoption across multiple segments and product lines.

Translating business requirements into scalable AI Solution data and analytics solutions, ensuring alignment with long-term strategy.

Delivering actionable insights to support customer acquisition, portfolio management, and hyper-personalized engagement.

Leading cross-functional teams, promote agile delivery practices, and build high-performing analytics capabilities across global teams.

Designing, developing, and maintaining interactive reports and dashboards using Databrick, Snowfalke, BI tools, Tableau, Power BI, Looker and provided actionable insights to stakeholders.

Collaborated with business units to gather requirements, translate them into technical specifications, and deliver high-quality BI solutions.

Building and optimizing AI Solution data models, ETL processes, and data pipelines to support reporting and analytics needs.

Ensuring AI Solution data accuracy, consistency, and reliability through validation and quality checks.

Partnering with AI Solution data engineering and IT teams to integrate data from multiple sources SQL databases, cloud platforms, APIs

Implemented best practices in data visualization and storytelling to enhance user adoption and understanding.

Contributing to AI Solution data governance initiatives, including defining data standards, policies, and ensuring compliance with regulations GDPR, CCPA

Troubleshooting and resolving issues related to BI tools, data discrepancies, or performance bottlenecks.

Proficient in AI Solution data modeling, ETL processes, and optimizing data workflows to support business intelligence and analytics initiatives.

Experience in designing and developing interactive dashboards using Excel, Tableau, Power BI and other cloud-based platforms

Results-driven AI Solution Data Architect with a proven track record of designing and implementing data architectures that drive business value.

Spearheaded the development of a cloud-based data warehouse that reduced data processing time by 50% and empowered business users with real-time analytics capabilities.

Collaborated with cross-functional teams to align data strategies with organizational goals and support data-driven decision making.

AI Solution Architecture: Enterprise Data Strategy & alignment to Business Strategy, Transformation Projects, various data and warehouse architecture models, Information flow, Integration and interfacing models and approach, Business Information Models, Logical Data Models, Canonical models, Data Life Cycle Management, Reporting Analytical, Compliance, Regulatory, Models

Skills in AI Solution Data Analysis, Data cleansing and transformation, Data domain knowledge, Data Integration, Data Management, Data Manipulation, Data Sourcing, Data strategy and governance, Data Structures and Algorithms, Data visualization and interpretation, Digital Security, Extract, transformation, upload load and download.

Familiarity with cloud platforms AWS, Azure, Google Cloud and their data services Redshift, BigQuery, Databricks, Snowflake

Databricks: Databricks clusters (setup, configuration, optimization), Notebooks (development, collaboration, scheduling), Delta Lake (implementation, management, optimization), Databricks SQL (analytics, reporting), Databricks Machine Learning (model development, deployment), Databricks workflows (automation, orchestration), Databricks Connect and Databricks Auto Loader

AI/GenAI Development and Advisory

Leading global bank GenAI Solution Architect and a pivotal role driving the bank’s generative AI strategy.

Leading the development, governance, and adoption of enterprise AI/ML solutions

Working with senior stakeholders and leveraging technologies like LLMs, MS OpenAI, and Azure AI.

Responsible for Project Strategic Planning & Governance

Defining and implementing the AI/GenAI roadmap aligned with business goals

Establish enterprise-wide AI governance frameworks and ensure compliance

Engaging stakeholders to promote AI/ML adoption and value creation

Responsible for Development and Advisory

Leading the design, development, and testing of innovative AI/GenAI solutions

Champion use cases involving LLMs, chatbots, and automation via Microsoft tools

Evaluated AI models, advise on architecture, and guide best practices

Responsible for Security and Risk Management

Ensuring AI solutions adhere to robust security and risk frameworks

Implemented governance protocols for responsible AI/ML usage

Responsible for Training and Enablement

Developed training programs to upskill teams on AI tools and capabilities

Build an internal AI/ML knowledge-sharing ecosystem

Responsible for Support and Maintenance

Provided ongoing support and continuous improvement of AI solutions

Experience in solution architecture in AI/ML platform integration, data pipeline design and API-driven systems

Assisted in designing AI solutions that support enterprise needs, including data pipelines, API workflows, and cloud-based model deployment

Worked under the guidance of staff architects to implement components of the AI platform, such as model registries, runtime environments, and tool integrations

Supported API integration efforts between AI services and business platforms using RESTful and event-driven patterns

Participated in developing and testing AI/ML workflows on platforms such as Azure ML, AI Foundry, or AWS SageMaker, Bedrock, etc

Document AI solution designs, architectural decisions, integration flows, and deployment procedures

Evaluated and recommend AI platforms and tools (e.g., Azure ML, AI Foundry, Databricks, open-source toolkits) based on enterprise goals and technical fit

Collaborated with data engineers, MLOps, and software teams to ensure smooth delivery of AI features and services

Contributed to proof-of-concepts (PoCs), technical evaluations, and prototyping efforts under supervision

Staying current with AI technologies and best practices in integration, model lifecycle management, and platform operations

Participated in architecture reviews, technical discussions, and sprint planning with cross-functional teams

Expertise designing and integrating end-to-end AI workflows, including data pipelines, model orchestration, and serving APIs across hybrid cloud environments

Experience in designing AI/ML systems, cloud-native architectures, and API ecosystems.

Hands-on experience with cloud-based AI services such as Microsoft Azure ML, AI Foundry, AWS SageMaker, Bedrock, including model deployment, monitoring, and scaling.

Experience with tools and frameworks such as LangChain, LangGraph, GraphRAG, Retrieval-Augmented Generation (RAG), MLflow, Kubeflow, and related AI/ML orchestration technologies.

Capable to evaluate and integrate new AI technologies, frameworks, and vendor solutions into enterprise environments.

Effectively collaborated, worked across data, API, ML or AI platform teams, with a clear communication style to bridge business and technical stakeholders.

Managing internal IT, Networks, business and external suppliers, government stakeholders

Data Modeling and Design:

Engineered a robust data warehouse solution that streamlined data processing and reporting tasks, saving over 20 hours per week for the analytics team.

Developed and maintained data lakes and analytical platforms using Databricks on AWS and Azure, ensuring scalability, data security, and automation of infrastructure as code (IaC).

Designed and implemented efficient ETL/ELT processes using Apache Spark, Airflow, and Databricks, tailored to various industry requirements including banking, medical, and e-commerce sectors.

Developed and maintained continuous integration and continuous deployment (CI/CD) pipelines for schema migrations, workflows, and cluster pools using tools like Git, Jenkins, Azure Repos, and Azure Pipelines.

Developed integration frameworks for FHIR format data and Azure Databricks, troubleshooting and optimizing Delta Live Tables jobs to ensure seamless data processing and integration.

Data Governance and Compliance

Designed and implemented a data governance framework for a healthcare provider, ensuring compliance with HIPAA regulations and reducing data breaches by 95%

Led a team of 5 data engineers to implement encryption and access controls for a financial services company, protecting sensitive customer data for over 10 million accounts

Implemented a comprehensive data governance framework compliant with GDPR, mitigating legal risk and achieving 100% audit success rate.

Developing data governance policies and procedures

Implementing data security measures such as encryption, access controls, and auditing

Ensuring compliance with regulations like GDPR, HIPAA, and SOC 2

Data Integration and ETL processed

Designed and executed ETL progress to integrated data from 10+ disparate sources, enhancing data accuracy by 30% and supporting real-time analytics.

Revamped legacy ETL workflows with modern, cloud-based solutions, cutting down data processing time by 10% and drastically reducing operational costs.

Designed a data lake architecture that reduced data processing time by 75% and saved the company $2 million annually

Developed a real-time data streaming solution that enabled a retail company to personalize product recommendations, increasing sales by 20%

Created a data virtualization layer that allowed a global organization to access data from multiple sources in real-time, improving decision-making and operational efficiency

Designed a data warehouse that reduced query response time by 90%, enabling real-time analytics for over 1,000 users

Implemented a data quality framework that improved data accuracy by 99.5%, reducing customer complaints by 75%

Led a team of 10 data architects to migrate 500 TB of data to the cloud, completing the project 2 months ahead of schedule and 15% under budget

Leadership and Team Management

Led a team of data analysts and engineers, ensuring they have the data repository, tools, resources, and training to deliver high-quality data analysis and reporting.

Partner with cross-functional stakeholders to understand their business needs and translate them into actionable data requests.

Develop and implement a data strategy for the commercial team that is aligned with the overall company objectives.

Design, build, and maintain data pipelines and dashboards to track key performance indicators (KPIs) and measure the effectiveness of commercial campaigns and treatments

Conduct in-depth data analysis to identify trends, opportunities, and areas for improvement in hotel and flight acquisition, conversion, and revenue.

Communicate complex data insights in a clear, concise, and actionable way to both technical and non-technical audiences

Stay up to date on the latest data science trends and technologies and implement them to improve data analysis capabilities.

Foster a collaborative and data-driven culture within the commercial team.

Leading a team of 15 data professionals in a high-stakes migration project, completing the projects 3 weeks ahead of schedule and under budget, earning the team an excellence award.

Led team of 15 data engineers and analysts to implement a data lake architecture, improving data access and reducing costs by 30%

Collaborated with business leaders to develop a data strategy that aligned with the company's overall goals, resulting in a 20% increase in revenue

Presented technical concepts to non-technical stakeholders, gaining buy-in for a $10 million data infrastructure project

Project Experience:

Client: BNP Paribas, France

Role: Solution Architect

Date: Sep/2023 to till date

Roles & Responsibilities:

●Lead end-to-end planning, execution, and delivery of the data transformation program, aligning with organizational objectives.

●Managing cross-functional teams including data engineering, data governance, IT, and business stakeholders.

●Collaborated closely with senior leadership, ensuring project milestones, budgets, and deliverables are met.

●Defining project scope, goals, and deliverables in collaboration with stakeholders and technical leads.

●Developed detailed project plans, risk registers, resource plans, and communication strategies.

●Overseeing vendor relationships, contracts, and third-party integrations as needed.

●Tracking project performance using appropriate tools and KPIs, ensuring continuous reporting to stakeholders.

●Identifying and mitigating risks, issues, and dependencies throughout the project lifecycle.

●Involved solution architecture in AI/ML platform integration, data pipeline design and API-driven systems

●Assisted in designing AI solutions that support enterprise needs, including data pipelines, API workflows, and cloud-based model deployment

●Worked under the guidance of staff architects to implement components of the AI platform, such as model registries, runtime environments, and tool integrations

●Supported API integration efforts between AI services and business platforms using RESTful and event-driven patterns

●Participated in developing and testing AI/ML workflows on platforms such as Azure ML, AI Foundry, or AWS SageMaker, Bedrock, etc

●Document AI solution designs, architectural decisions, integration flows, and deployment procedures

●Evaluated and recommend AI platforms and tools (e.g., Azure ML, AI Foundry, Databricks, open-source toolkits) based on enterprise goals and technical fit

●Collaborated with data engineers, MLOps, and software teams to ensure smooth delivery of AI features and services

●Contributed to proof-of-concepts (PoCs), technical evaluations, and prototyping efforts under supervision

●Staying current with AI technologies and best practices in integration, model lifecycle management, and platform operations

●Participated in architecture reviews, technical discussions, and sprint planning with cross-functional teams

●Worked and created designing and integrating end-to-end AI workflows, including data pipelines, model orchestration, and serving APIs across hybrid cloud environments

●Worked and created designing AI/ML systems, cloud-native architectures, and API ecosystems.

●Hands-on experience with cloud-based AI services such as Microsoft Azure ML, AI Foundry, AWS SageMaker, Bedrock, including model deployment, monitoring, and scaling.

● Worked with tools and frameworks such as LangChain, LangGraph, GraphRAG, Retrieval-Augmented Generation (RAG), MLflow, Kubeflow, and related AI/ML orchestration technologies.

●Evaluated and integrated new AI technologies, frameworks, and vendor solutions into enterprise environments.

●Effectively collaborated, worked across data, API, ML or AI platform teams, with a clear communication style to bridge business and technical stakeholders.

●Managing internal IT, Networks, business and external suppliers, government stakeholders

●Fostering a culture of agility, innovation, and accountability within the project team.

●Hands on project management in data-related with enterprise IT projects

●Managing large-scale data transformation with digital modernization programs.

●Strong understanding of data platforms e.g., data lakes, cloud data warehouses, ETL pipelines, and data governance frameworks.

●Responsible for project communication, stakeholder management, and leadership.

●Comfortable working in fast-paced, cross-functional, and matrixed environments

●Strong understanding of data modeling concepts and able to design various components of data model and data engineering solution.

●Experience in designing and developing interactive dashboards using Excel, Tableau, PowerBI and other cloud-based platforms

●Building and testing data pipelines, with a solid foundation in unit and integration testing

●Hands-on experience with AWS and Kubernetes for cloud-based and containerized deployments.

●Working with data warehouses such as Snowflake, Apache Spark, or Hive, and orchestration tools like Apache Airflow, Dagster, and Prefect.

●Databricks: Databricks clusters (setup, configuration, optimization), Notebooks (development, collaboration, scheduling), Delta Lake (implementation, management, optimization), Databricks SQL (analytics, reporting), Databricks Machine Learning (model development, deployment), Databricks workflows (automation, orchestration), Databricks Connect and Databricks Auto Loader

●Comfortable with DevOps practices, GitHub workflows, CI/CD pipelines, and Agile development methodologies

●Strong in analytical and problem-solving.

Client: Vodafone, UK

Role: Solutions Architect

Date: Aug/2021 to Sep/2023

Roles & Responsibilities:

●Leading global bank GenAI Solution Architect and a pivotal role driving the bank’s generative AI strategy.

●Leading the development, governance, and adoption of enterprise AI solutions

●Working with senior stakeholders and leveraging technologies like LLMs, MS OpenAI, and Azure AI.

●Responsible for Project Strategic Planning & Governance

●Define and implement the AI/GenAI roadmap aligned with business goals

●Establish enterprise-wide AI governance frameworks and ensure compliance

●Engage stakeholders to promote AI adoption and value creation

●Responsible for Development and Advisory

●Experience in designing and developing interactive dashboards using Excel, Tableau, PowerBI and other cloud-based platforms

●Lead the design, development, and testing of innovative AI/GenAI solutions

●Champion use cases involving LLMs, chatbots, and automation via Microsoft tools

●Evaluate AI models, advise on architecture, and guide best practices

●Responsible for Security and Risk Management

●Ensure AI solutions adhere to robust security and risk frameworks

●Implement governance protocols for responsible AI usage

●Responsible for Training and Enablement

●Develop training programs to upskill teams on AI tools and capabilities

●Build an internal AI knowledge-sharing ecosystem

●Responsible for Support and Maintenance

●Provide ongoing support and continuous improvement of AI solutions

●Databricks: Databricks clusters (setup, configuration, optimization), Notebooks (development, collaboration, scheduling), Delta Lake (implementation, management, optimization), Databricks SQL (analytics, reporting), Databricks Machine Learning (model development, deployment), Databricks workflows (automation, orchestration), Databricks Connect and Databricks Auto Loader

Client: Wise (TransferWise), London, UK

Role: AI and Data Practice Lead

Date: Jul/2020 to Sep/2021

Roles & Responsibilities:

●Involved solution architecture in AI/ML platform integration, data pipeline design and API-driven systems

●Assisted in designing AI solutions that support enterprise needs, including data pipelines, API workflows, and cloud-based model deployment

●Worked under the guidance of staff architects to implement components of the AI platform, such as model registries, runtime environments, and tool integrations

●Supported API integration efforts between AI services and business platforms using RESTful and event-driven patterns

●Participated in developing and testing AI/ML workflows on platforms such as Azure ML, AI Foundry, or AWS SageMaker, Bedrock, etc

●Document AI solution designs, architectural decisions, integration flows, and deployment procedures

●Evaluated and recommend AI platforms and tools (e.g., Azure ML, AI Foundry, Databricks, open-source toolkits) based on enterprise goals and technical fit

●Collaborated with data engineers, MLOps, and software teams to ensure smooth delivery of AI features and services

●Contributed to proof-of-concepts (PoCs), technical evaluations, and prototyping efforts under supervision

●Staying current with AI technologies and best practices in integration, model lifecycle management, and platform operations

●Participated in architecture reviews, technical discussions, and sprint planning with cross-functional teams

●Worked and created designing and integrating end-to-end AI workflows, including data pipelines, model orchestration, and serving APIs across hybrid cloud environments

●Worked and created designing AI/ML systems, cloud-native architectures, and API ecosystems.

●Hands-on experience with cloud-based AI services such as Microsoft Azure ML, AI Foundry, AWS SageMaker, Bedrock, including model deployment, monitoring, and scaling.

● Worked with tools and frameworks such as LangChain, LangGraph, GraphRAG, Retrieval-Augmented Generation (RAG), MLflow, Kubeflow, and related AI/ML orchestration technologies.

●Evaluated and integrated new AI technologies, frameworks, and vendor solutions into enterprise environments.

●Effectively collaborated, worked across data, API, ML or AI platform teams, with a clear communication style to bridge business and technical stakeholders.

●Databricks: Databricks clusters (setup, configuration, optimization), Notebooks (development, collaboration, scheduling), Delta Lake (implementation, management, optimization), Databricks SQL (analytics, reporting), Databricks Machine Learning (model development, deployment), Databricks workflows (automation, orchestration), Databricks Connect and Databricks Auto Loader

●Managing internal IT, Networks, business and external suppliers, government stakeholders

●Analytics Strategy & Execution – Collaborated with the Group Finance Data & Analytics team to design and implement a robust analytics strategy.

●Treasury Alignment – Partnering with senior Treasury leaders to ensure analytics tools are aligned and data issues are mitigated.

●Data Framework & Governance – Developed a structured data framework, covering governance, architecture, risk management, and data sourcing.

●Roadmap Development – Created a strategic roadmap for delivering the Treasury analytics vision.

●System Implementation – Overseeing the execution of Treasury Data and Analytics systems, ensuring compliance with group standards.

●Financial & Program Management – Managing financials, governance, and delivery oversight of analytics projects.

●Risk & Compliance – Ensuring analytics demand within Treasury aligns with strategic goals and mitigates data risks.

●Experience in designing and developing interactive dashboards using Excel, Tableau, PowerBI and other cloud-based platforms

●Business Reporting & Self-Service Analytics – Facilitated enhanced business finance reporting and empower teams with advanced self-service analytics tools.

●Advanced Treasury Programs – Supported key Treasury initiatives such as Balance Sheet Management and the Genesis Program.

●AI & Innovation – Leading AI-driven initiatives within Treasury analytics.

●Centre of Excellence & Upskilling – Contributed to the Analytics Centre of Excellence and build Treasury’s capabilities in modern analytics tools.

Client: Mattson Technology Singapore Pte Ltd, Singapore

Role: Data Lead

Date: Apr/2019 to Jul/2021

Roles & Responsibilities:

●Conducted Data Governance to enhance company-wide understanding of critical data, increasing data quality and customer trust by 30%

●Defining and implemented the AI and data practice vision, strategy, and roadmap, aligned with the company's goals and values, focusing on leveraging data analytics and AI/ML to drive insights-driven decision making

●Developed and articulated a comprehensive strategy for the new Data and AI business line, including market analysis, value proposition, and business model

●Building a strong pipeline with the acquisition of 10 new wins and achieving at least USD 1M in revenue for Data and AI opportunities

●Experience in designing and developing interactive dashboards using Excel, Tableau, PowerBI and/or other cloud-based platforms

●Collaborated with strategic partners, vendors, solution development, business development, sales, and delivery teams, to create cross-functional synergies and opportunities for growth

●Collaborated with internal and external stakeholders across the region to gain buy-in and support for the new Data and AI line of business.

●This includes presenting senior leadership, negotiating with partners, and engaging with customers

●Overseeing the design, development, and delivery of AI and data solutions for projects, ensuring high quality, scalability, and impact

●Engaging with existing and potential clients, understanding their business needs and challenges, and proposing AI and data solutions that create value and competitive advantage

●Worked closely with product development and marketing teams to ensure the offerings meet market needs

●Overseeing the execution of the strategy, adjusting as necessary based on performance data and market feedback.

●Ensuring milestones are met on time and within budget

●Stay abreast of the latest trends, technologies, and best practices in AI and data, and leverage them to enhance our offerings and capabilities

●Involved solution architecture in AI/ML platform integration, data pipeline design and API-driven systems

●Assisted in designing AI solutions that support enterprise needs, including data pipelines, API workflows, and cloud-based model deployment

●Worked under the guidance of staff architects to implement components of the AI platform, such as model registries, runtime environments, and tool integrations

●Supported API integration efforts between AI services and business platforms using RESTful and event-driven patterns

●Participated in developing and testing AI/ML workflows on platforms such as Azure ML, AI Foundry, or AWS SageMaker, Bedrock, etc

●Document AI solution designs, architectural decisions, integration flows, and deployment procedures

●Evaluated and recommend AI platforms and tools (e.g., Azure ML, AI Foundry, Databricks, open-source toolkits) based on enterprise goals and technical fit

●Collaborated with data engineers, MLOps, and software teams to ensure smooth delivery of AI features and services

●Contributed to proof-of-concepts (PoCs), technical evaluations, and prototyping efforts under supervision

●Staying current with AI technologies and best practices in integration, model lifecycle management, and platform operations

●Participated in architecture reviews, technical discussions, and sprint planning with cross-functional teams

●Worked and created designing and integrating end-to-end AI workflows, including data pipelines, model orchestration, and serving APIs across hybrid cloud environments

●Worked and created designing AI/ML systems, cloud-native architectures, and API ecosystems.

●Hands-on experience with cloud-based AI services such as Microsoft Azure ML, AI Foundry, AWS SageMaker, Bedrock, including model deployment, monitoring, and scaling.

● Worked with tools and frameworks such as LangChain, LangGraph, GraphRAG, Retrieval-Augmented Generation (RAG), MLflow, Kubeflow, and related AI/ML orchestration technologies.

●Evaluated and integrated new AI technologies, frameworks, and vendor solutions into enterprise environments.

●Databricks: Databricks clusters (setup, configuration, optimization), Notebooks (development, collaboration, scheduling), Delta Lake (implementation, management, optimization), Databricks SQL (analytics, reporting), Databricks Machine Learning (model development, deployment), Databricks workflows (automation, orchestration), Databricks Connect and Databricks Auto Loader

●Effectively collaborated, worked across data, API, ML or AI platform teams, with a clear communication style to bridge business and technical stakeholders.

●Managing internal IT, Networks, business and external suppliers, government stakeholders

Client: Scotiabank, Ontario, Canada

Role: Principal Data Engineer

Date: Nov/2017 to Apr/2019

Roles & Responsibilities:

●As a Data Solution Architect and Project Manager part of Financial Information Management (FIM) within Group Finance under the Change Management organization structure.

●Key responsibilities include, Gathering and documenting user requirements

●Assessing functional impact of changes to Finance applications

●Assessing the user configuration setup required for application changes

●Reviewing functional design and ensure that business requirements are met

●Responsible for end-to-end User Acceptance Testing activities such as defining the test approach, preparing test scripts and executing User Acceptance Test, analyzing test results and ensuring that reported issues are resolved

●Transition project to production support teams with proper documentation and training

●Provided support in resolving post implementation issues during warranty period

●Business analysis and developing business requirements documents, test strategy and test scripts for User Acceptance Test

●Experience in designing and developing interactive dashboards using Excel, Tableau, PowerBI and other cloud-based platforms

●Proficiency in SQL and strong understanding of Data Architecture, Data Mapping, Data Warehousing, and strong ability in querying and analyzing data

●Worked in Data Modelling, Object Modelling, Design Patterns, extraction, transformation, and loading (ETL) processes.

●Worked with legacy systems and knowledge of relevant data formats such as flat files, Excel, SQL databases.

●Worked with master data such material, customer, vendor and transactional data migration processes.

●Proficient in data analysis and validation tools such as MS Excel and preferably SQL and Python.

●Unit testing and Integration testing of the deliverables.

●Provide support to business users during the UAT phase of the project and resolve issues if any

●Building data pipelines

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