• Designing and implementing AI solutions aligned with business needs and technological standards. • Applying techniques like Retrieval-Augmented Generation (RAG) to improve the precision and relevance of AI interactions. • Developing Agentic RAGs, incorporating autonomous agents to refine data retrieval and content generation. • Creating modular architectures for agents specializing in tasks like information retrieval, contextual inference, and data-driven decision-making. • Leveraging Large Language Models (LLMs) such as Gemini and Llama for text generation and question answering. • Designing and managing data ingestion pipelines, and implementing serverless architectures and Pub/Sub models to scale data intake. • Using DevOps and MLOps practices for automating the deployment and management of AI models. • Integrating APIs to connect AI models with external systems and to register events and metadata. • Optimizing performance by analyzing system performance, detecting bottlenecks, and implementing caching strategies. • Collaborating with designers and product teams to ensure AI solutions meet real business and user needs, while maintaining innovation through continuous research and experimentation.

• Develop and maintain the Python codebase for the core automation engine, including data ingestion pipelines, standardization logic, and matching algorithms • Design and implement ETL middleware solutions that process, validate, and transform data from multiple sources with high accuracy and performance • Build Excel and PDF output pipelines using pandas, openpyxl, and xlsxwriter to generate comprehensive reports and reconciliation documents • Create responsive frontend components using React.js that enable users to interact with automation workflows and visualize results • Translate business rules into scalable code by collaborating with stakeholders to understand matching scenarios, reconciliation logic, and classification requirements • Write comprehensive unit tests using pytest to ensure code quality, reliability, and maintainability across the full stack • Document matching rules and reconciliation scenarios based on stakeholder discussions, serving as a bridge between technical implementation and business requirements • Collaborate with senior developers to architect solutions that balance performance, scalability, and maintainability

• Enhance the team’s capacity to deliver assigned stories by end of sprint • Deliver Value: Develop software features for relatively complex features with the support of more senior engineers during AgileDev engagements and address defects and enhancement requests. • Develop Collaboratively: Participate in successful Agile delivery by seeking guidance, raising concerns, advancing best practices, identifying blockers, and mentoring peers and more junior team members. • Become Fullstack: Demonstrate competency at two of the following: building attractive user interfaces, building data driven REST APIs, administering databases/cloud/servers. • Communicate Value: Collaborate with client stakeholders during sprint planning to finalize and document technical approach with user stories as subtasks. Discuss (not just accept) requirements, iterate. Communicate status. • Key Activities: Ensuring the team understands acceptance criteria, tracking daily progress, limit team’s QA kickback, deliver stories as assigned, find opportunities to increase capacity and velocity.

• Enhance the team’s capacity to deliver assigned stories by end of sprint. • Develop software features for relatively complex features with the support of more senior engineers during AgileDev engagements and address defects and enhancement requests. • Participate in successful Agile delivery by seeking guidance, raising concerns, advancing best practices, identifying blockers, and mentoring peers and more junior team members. • Demonstrate competency at two of the following: building attractive user interfaces, building data driven REST APIs, administering databases/cloud/servers. • Collaborate with client stakeholders during sprint planning to finalize and document technical approach with user stories as subtasks. Discuss (not just accept) requirements, iterate. Communicate status. • Ensuring the team understands acceptance criteria, tracking daily progress, limit team’s QA kickback, deliver stories as assigned, find opportunities to increase capacity and velocity.