Architech

Cloud Engineer

• Design and implement solution architectures that satisfy client requirements, with a strong focus on AI-enabled and cloud-native applications
• Design AI-enabled workflows such as chatbots, search, summarization, and automation
• Leverage RAG (retrieval-augmented generation), agentic AI patterns, vector databases, and enterprise search in applied solutions
• Orchestrate multi-service AI solutions that combine LLMs, APIs, and cloud services
• Guide clients and teams through cloud modernization projects
• Understand data security and residency requirements
• Ensure solutions comply with security and regulatory standards while managing technical risks across architectures
• Support Services teams during delivery
• Build prototypes and participate in demos with clients
• Create solution diagrams and architectures for client decks to support pre-sales discussions
• Partner with clients in pre-sales support: participate in meetings, capture requirements, and collaborate with the sales lead
• Communicate architectural decisions clearly to both business and technical audiences
• Balance functional and non-functional requirements
• Support scoping and estimation for solutions across multiple industries
• Collaborate closely with technical leads, developers, designers, strategists, and project managers to deliver high-quality solutions
• Leverage Architech’s cloud and technology partners to strengthen solution proposals
• Work directly with mid-sized and enterprise clients
• Embrace and contribute to the achievement of Architech’s sustainability goals

AI and Cloud Architect

Data Engineer

• Architect Microsoft Fabric environments: Design end-to-end data architectures, defining ingestion, transformation, and curation patterns that support analytics and AI workloads.
• Design and manage data lake architectures: Ensure efficient replication, synchronization, and data flow across Fabric workspaces and multi-zone environments.
• Framework alignment: Ensure all data architectures align with Microsoft’s Cloud Adoption Framework (CAF) and the Azure Well-Architected Framework to maintain consistency, scalability, and governance across solutions.
• Define business-aligned data models: Partner with stakeholders to understand reporting, analytics, and AI needs and design scalable, flexible data models.
• AI readiness: Define structures, metadata, and access patterns that make data discoverable and usable for AI workloads such as retrieval-augmented generation (RAG), intelligent search, and summarization.
• Data governance and security: Collaborate with peers to implement encryption at rest and in transit, role-based access controls (RBAC), and data masking that meet enterprise and client expectations for data integrity and privacy.
• Performance and cost optimization: Guide architecture decisions related to Fabric SKUs, OneLake storage, and data refresh strategies for efficient scale and cost.
• Collaboration and mentorship: Work closely with Data Engineers to translate architecture into delivery, promote data quality, and ensure design consistency.
• Documentation and enablement: Produce reference architectures, blueprints, and reusable standards that accelerate future projects and maintain governance consistency.

Data Architect – Microsoft Azure, Microsoft Fabric

Machine Learning Engineer

• Lead the development and optimization of churn prediction models to identify customers at risk of leaving
• Responsibilities include building scalable machine learning models, designing feature engineering pipelines, monitoring performance, and collaborating with business teams to translate insights into retention strategies

Product Manager

• Lead the end-to-end project delivery across Digital Serve & Support product portfolio
• Manage the lifecycle of product from ideation, discovery, design, deployment, and launch to ongoing support
• Drive the adoption and integration of AI solutions to enhance customer experience
• Develop and execute digital product strategies, leveraging data analytics
• Build strong relationships with business leaders, application owners, and technical teams
• Monitor product performance using key metrics and drive iterative improvements

Product Owner – Contract

Artificial Intelligence

• Pipeline Development & Market Engagement
• Consultative Discovery & Solution Advisory
• Engagement Structuring & Growth Leadership
• Partner Collaboration & Go-To-Market Execution
• Market Insights & Thought Leadership

AI and Cloud Architect

Data Architect – Microsoft Azure, Microsoft Fabric

Machine Learning Engineer

Product Owner – Contract

VP, AI Advisory