Hire Iman A. - AI Engineer - New York

Productivity Agent: Architected and deployed a production grade AI-powered productivity analytics platform ingesting GitLab telemetry to surface DORA metrics, delivery forecasts, and bottleneck signals for engineering leadership. Engineered gradient boosting models with custom feature pipelines, confidence scoring, and graceful fallback logic to ensure reliability at scale. Operationalized evidence based productivity levers (i.e AI assisted coding, backlog hygiene, and automated testing) into a scenario planning engine that enables data driven sprint planning and continuous improvement decisions. Application Rationalization: Architected an enterprise scale application scoring and simulation pipeline combining PCA based TIME classification (business value vs. technical health), NLP driven redundancy detection (weighted Jaccard + sentence-transformer similarity), autoencoder anomaly scoring, and multi dimensional signals across usage, compliance, and dependency graphs, all unified via weighted MCDA aggregation with rule based governance guardrails. Augmented recommendations with GenAI generated rationales to make outputs transparent, auditable, and actionable for portfolio decision makers.

* Worked closely with business stakeholders to develop and deploy multiple AI/ML products to address business challenges. Developed an AI-driven mapping API that analyzes unstructured text and automatically extracts and classifies key themes and categories (e.g. 5Ws, taxonomy/risk themes) by designing and fine-tuning advanced prompts for OpenAI’s LLM, delivering highly accurate, scalable text classification. Leveraged AI & Generative AI to auto-generate commentary based on pattern recognition of historical comments and performing calculations on new complex datasets. Fully automated a manual process and streamlined operations by reducing ~40 mins of daily effort for key business stakeholders, enhancing productivity and efficiency. Facilitated UBS-Credit Suisse merger integration by building and deploying an intelligent pipeline that delivers AI-driven recommendations for mapping similar controls, effectively reducing redundancy. Applied Retrieval Augmented Generation (RAG) technique with Azure OpenAI to allow business stakeholders to search and chat based on relevant data. Built pipelines to preprocess, parse, chunk, embed and index a large volume of documents of different types (docx, json, xlsx, pptx, pdf) from Azure blob storage. Utilized SQLAlchemy, Azure Functions, and custom scripting to perform efficient database manipulation, automate workflows, and integrate data processing tasks within cloud-based applications. Implemented multiple web applications using React, Node.Js & Flowable to provide an interactive and intuitive user interface to enable stakeholders to utilize AI/ML solutions. Developed RESTful APIs, enabling seamless communication between the frontend and backend through secure URL endpoints. Deployed applications by creating Docker images, containerizing them, and managing their execution in isolated Docker environments on the server.

* Sales+, an app to facilitate trading (Credit Sales team) * Implemented the useful feature of capturing notes, increasing convenience and practicality of the already existing Sales+ desktop app. * Allows front end office users to save additional information about their clients, such as accuracy of system (AI) recommended transactions and/or feedback from clients etc. * Full stack project, including generating React components to allow users to submit text, as well as save/retrieve from the database while connecting and interacting between multiple different microservices/APIs. * Deployed to ‘Test’ environment. * Smart Synthesizer (Group project using C#) * Developed an intelligent simulator to examine workflows across all microservices leveraged when booking trades. Detected log anomalies and categorized root causes for exceptions/errors encountered in the trading environments.