Senior AI Software Engineer

At Mattress Firm, I contributed to SleepExpert.AI and related AI platforms, building and scaling LLM-powered systems that support retail associates, customer interactions, and internal knowledge discovery across thousands of stores. - Designed and productionized LLM-powered recommendation and guidance systems used by 6,000+ in-store associates, improving associate enablement and reducing onboarding and training time by ~30%. - Built backend services and orchestration layers integrating retrieval pipelines, embedding-based semantic search, and contextual reasoning, supporting sub-100ms query latency at peak usage. - Implemented scalable semantic search systems indexing millions of product, policy, and operational documents, increasing knowledge retrieval accuracy and reducing internal support queries by ~25%. - Developed AI-assisted customer support services (summarization, intent detection, knowledge-grounded responses), reducing average handling time by ~35% and improving response consistency. - Optimized inference performance and batching strategies to reduce model serving latency by ~40% while maintaining response quality. - Implemented observability, error handling, and fallback strategies to ensure graceful degradation and >99.9% availability for AI-dependent workflows. - Collaborated with product, data, and engineering teams to take AI initiatives from prototype to stable, maintainable production systems with clear ownership and operational metrics.

At Citigroup, I worked on enterprise-scale AI and backend systems supporting fraud prevention, risk assessment, and compliance automation within a highly regulated financial environment. - Enhanced ML-driven fraud detection platforms by improving Python-based ETL pipelines and feature engineering workflows, increasing model accuracy by ~25% and reducing false positives by ~30%. - Built FastAPI-based microservices exposing fraud risk scores and signals to downstream decision engines, supporting millions of transactions per day with low-latency inference. - Designed scalable ML service architectures emphasizing auditability, security controls, and regulatory compliance across AML and onboarding workflows. - Delivered LLM-powered document understanding and summarization capabilities used in compliance review, reducing manual review time by ~3x. - Implemented automated data ingestion, model orchestration, and deployment pipelines to support continuous experimentation and faster model iteration. - Partnered with ML, risk, compliance, and platform teams to safely integrate AI outputs into production systems using Docker, CI/CD, and cloud-native deployment practices.

At IBM / Kyndryl, I contributed to Kyndryl Bridge, an AI-powered AIOps platform delivering predictive insights and automation across complex hybrid cloud and enterprise IT environments. - Built backend services and data-processing pipelines supporting large-scale telemetry ingestion, processing tens of millions of events per day from logs, metrics, and performance signals. - Applied ML-driven analytics to detect anomalies and predict infrastructure failures, helping enterprise clients reduce critical incidents by up to ~90% in production environments. - Developed services enabling real-time observability and AI-driven insights, delivering over 10M+ actionable insights per month across customer environments. - Integrated AI-based decisioning into automated remediation workflows, reducing mean time to resolution (MTTR) by ~40–60%. - Supported hybrid cloud modernization initiatives, improving system scalability, resilience, and operational efficiency for Fortune 500 clients. - Collaborated with platform, cloud, and operations teams to align AI outputs with reliability, cost, and performance objectives.

At Infosys, this role represented a transition from full-stack and backend engineering into applied data and AI-oriented systems, where I began working more deeply with data pipelines, analytics platforms, and ML-enabled applications in enterprise environments. - Led backend modernization efforts for large-scale enterprise platforms, gradually expanding responsibilities into data-heavy and analytics-driven components. - Built and maintained cloud-native backend services while collaborating with data teams on feature pipelines and analytics integrations. - Designed and implemented data ingestion and transformation workflows that fed downstream analytics and early ML use cases. - Worked on data-driven platforms supporting forecasting, reporting, and decision-support systems for global clients. - Improved batch data processing and integration workflows, reducing processing times by ~30% and increasing data reliability. - Collaborated with cross-functional teams to bridge traditional software engineering with emerging ML and analytics requirements, laying the foundation for later AI-focused roles.

- Developed and maintained large-scale consumer-facing applications supporting TV, mobile, and connected services used by millions of Xfinity customers. - Built frontend features using React and Angular, integrating with backend APIs to deliver responsive, high-availability user experiences. - Implemented and maintained backend services supporting customer account management, service provisioning, and content delivery workflows. - Collaborated with product, QA, and infrastructure teams to improve performance, reliability, and deployment stability across consumer platforms.

- Promoted after internship to support large-scale CHI Connect modernization across 100+ locations. - Improved front-end responsiveness by ~25% and reduced maintenance overhead by ~30%. - Collaborated with cross-functional stakeholders to optimize UI workflows used by thousands of staff.

- Built Angular/JS UI components supporting CHI’s internal tooling modernization. - Improved usability and reduced UI errors by ~15% through interface updates.