Alireza Manashty

• Led the design and implementation of GenAI and ML solutions for a major financial institution with over $100B in assets. • Provided organization-level standards and support for Business Intelligence (BI) and enterprise teams. • Collaborated with cross-functional teams to enhance operational efficiency and drive innovation in AI applications.

ML Architecture | MLOps Leadership | Platform Strategy | Cloud-Native AI 🏗️ ML Target Architecture & MLOps Leadership: Designed the enterprise ML architecture—featuring online/offline Feature Store, Databricks pipelines, LLM/model serving, and data governance—and led MLOps modernization with scalable, modular pipelines and CI/CD integration. 🎯 Strategic Alignment & Platform Adoption: Defined and owned the AI/ML architecture vision aligned with enterprise data goals; facilitated cross-functional adoption with clear integration strategies. 🌀 AI/ML Lifecycle Standardization: Established lifecycle best practices for ML deployment, model versioning, explainability, and performance monitoring across reusable architectural components. 👨‍🏫 Mentorship & Technical Enablement: Mentored developers and solution architects to strengthen ML platform adoption, design thinking, and responsible AI practices across squads. ☁️ Cloud-Native Innovation & Research: Applied cutting-edge advancements in cloud-native AI, LLMOps, and feature engineering to influence platform roadmap and tooling strategies.

Strategic Architecture | API Design | Cross-Functional Delivery | Operational Efficiency 🏛️ Strategic Architectural Leadership: Led the architecture and roadmap for the Risk Assist API v1 and Control Group, significantly improving data integration and operational efficiency. 🧩 Innovative Project Oversight: Acted as End-to-End lead on key initiatives, collaborating with Rada.R and LEGO teams to drive advancements in Load and Unit Testing processes. ⚙️ Efficiency through Design: Delivered a 300% reduction in API maintenance requirements through scalable architectural improvements in the Control Group design. 📐 Comprehensive Solution Framework: Built and rolled out a reusable solution design framework, minimizing future API change efforts and accelerating project delivery timelines. 🤝 Cross-Functional Collaboration: Served as architectural owner—reviewing end-to-end solution designs and integrating major enhancements across modeling and development workflows.

AI Product Leadership | Voice Interfaces | Contract Intelligence | LLMOps 🧠 AI Solution Leadership: Led a team of developers to deliver a voice-enabled, end-to-end AI solution for construction document understanding and workflow automation. 📄 Agentic Contract Processing: Designed and deployed an agent-based AI system for construction contract interpretation, integrated into enterprise-level document intelligence workflows. 🌐 Multilingual Integration: Enabled real-time support for 10+ spoken languages and integrated the solution with leading construction tech platforms including Procore and Autodesk. 📱 Voice-First App Development: Built cross-platform applications using .NET MAUI, Azure AI Studio, and OpenAI—enabling voice-driven form filling, retrieval-augmented generation (RAG), and task automation. 🛠️ Prompt Engineering & LLMOps: Crafted prompts for advanced agent workflows and maintained responsible AI practices through model monitoring and content safety validations.

Applied AI | Transcription Systems | LLM Cost Optimization | Forecasting & Automation 🎙️ AI-Driven Transcription Platform: Built a near real-time, containerized transcription pipeline using Docker, AWS Fargate, SQS, and Whisper. Processed 10K+ RingCentral calls/day with an 8× performance boost. 💡 Prompt Optimization for LLMs: Minimized inference costs by engineering efficient prompts that preserved output quality and throughput while significantly reducing token usage. 📊 Information Extraction & Attribute Engineering: Designed 100+ domain-specific attributes across healthcare, finance, and leasing to support automated insight generation and structured reporting. 🌍 Cross-Domain Leadership: Led and mentored a team of client-facing data scientists delivering AI solutions across healthcare, financial services, and commercial leasing. 📈 Forecasting & Data Automation: Developed a forecasting model using MongoDB Atlas usage data and deployed automated pipelines on EC2 within a secure VPC to move billing data into S3.

📊 Marketing Data Pipeline Development: Designed and implemented a scalable data pipeline from Google Analytics 4, processing 15M+ monthly events with high data quality and integrity. 🚀 30× Pipeline Acceleration: Reduced pipeline runtime from 30 days to under 1 day by optimizing compute infrastructure—migrated workloads to GPU-based processing, dramatically lowering costs while boosting throughput. 💰 Cost-Efficient Scaling: Achieved significant cost reduction through performance tuning and cloud resource calibration, enabling sustainable, high-volume analytics at scale.

Note: Contract position via Unify Consulting 🧠 AI for Marketing Recommendations: Led a team of data scientists to design a transformer interpretability model for personalized marketing recommendations. 🔁 MLOps & Pipeline Development: Built and maintained machine learning and deep learning pipelines using Azure Machine Learning and Python for robust model deployment. 📈 Predictive Modeling: Worked with large-scale datasets to develop, evaluate, and optimize advanced predictive models and algorithms. 🤝 Cross-Functional Collaboration: Partnered with product engineers to scale AI prototypes into production-ready services.

Google x Coursera Labs: Developed hands-on data science lab content for Google’s Coursera specialization, focusing on practical, project-based learning experiences aligned with industry standards. 🛠️ Curriculum Design: Created interactive Jupyter Notebook-based labs and auto-graded assessments covering Python programming, data analysis, and applied machine learning workflows. 📚 Learning Experience Optimization: Ensured labs were accessible, engaging, and aligned with adult learning principles—supporting thousands of learners globally through scalable, high-impact content. 🌐 Collaborative Delivery: Worked closely with instructional designers, SMEs, and Google education teams to translate complex concepts into digestible, real-world exercises.

Assistant Professor (Data Science & Machine Learning) at the Department of Computer Science, University of Regina Director of Data Science Laboratory (urdatascience.ca) 🔬 Research & Impact: Designed and tested data-driven hypotheses to enhance curricula and research outcomes in data science and statistics. 💰 Funding: Secured $600K+ in research funding from NSERC, MITACS, Microsoft, Nvidia, and Kaggle. 👥 Leadership & Talent Development: Led 5 federally funded research projects. Supervised and mentored 20+ graduate and undergraduate students who went on to roles at Google, EA, Nokia, Disney+, and FCC. 📚 Teaching Experience: Delivered courses in Python & Data Fundamentals, Foundations of Data Science, and C++ Programming & Problem Solving. 📝 Proposal Development: Contributed to 20+ interdisciplinary research proposals, including project planning, Gantt charts, budgets, fallback plans, and deliverables. 🤝 Team Building: Established and managed 7+ diverse, interdisciplinary research teams since 2020. 💼 Budget & Asset Oversight: Managed multi-year operating and project budgets, overseeing 200+ research assets. 📈 Performance & Mentorship: Conducted performance reviews and provided training to 500+ students across various roles and levels. 📊 Agile Project Management: Facilitated bi-weekly sprint planning using OKRs and the Eisenhower Matrix. 🧑‍💼 Hiring & Admissions: Interviewed 100+ candidates and hired 30+ student employees. Reviewed over 500 supervision requests, 200+ program applications, and 80+ faculty/graduate applications across multiple committees.

Founder and director of Data Science Laboratory at University of Regina. Received more than 0.6M of public public and national funding and managed more than 15 researchers. Provided data science, machine learning, and AI consultation to industry across the country. Alumni of this lab now work at Google, Electronic Arts, and SaskTell and study at universities such as University of Southern California. Along with NSERC and MITACS, also received funding from Google Kaggle, NVidia, and Microsoft corporation.

Designing data integration solutions and architecture Developing data science strategy for the corporate Migrating data analytics platforms to cloud