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I’m Aziz Rizvi, a Lead AI/ML Engineer with 12+ years of experience building scalable data platforms, production machine learning systems, and enterprise-grade Generative AI solutions. My work spans LLM orchestration, Retrieval-Augmented Generation (RAG), agentic/autonomous workflows, and low-latency inference architectures—helping teams turn structured and unstructured enterprise data into reliable intelligence. I’ve led end-to-end implementations across fintech, healthcare, and sales/customer intelligence, including semantic retrieval with vector databases, fine-tuning pipelines, and streaming AI systems using Kafka/Spark. I’m especially focused on MLOps/LLMOps best practices—evaluation, hallucination reduction, governance, and observability—so AI copilots and automation deliver measurable business impact at enterprise scale.…I’m Aziz Rizvi, a Lead AI/ML Engineer with 12+ years of experience building scalable data platforms, production machine learning systems, and enterprise-grade Generative AI solutions. My work spans LLM orchestration, Retrieval-Augmented Generation (RAG), agentic/autonomous workflows, and low-latency inference architectures—helping teams turn structured and unstructured enterprise data into reliable intelligence. I’ve led end-to-end implementations across fintech, healthcare, and sales/customer intelligence, including semantic retrieval with vector databases, fine-tuning pipelines, and streaming AI systems using Kafka/Spark. I’m especially focused on MLOps/LLMOps best practices—evaluation, hallucination reduction, governance, and observability—so AI copilots and automation deliver measurable business impact at enterprise scale.

Aziz Rizvi

AI Engineer, Data Scientist, Software Engineer, +2

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I’m Aziz Rizvi, a Lead AI/ML Engineer with 12+ years of experience building scalable data platforms, production machine learning systems, and enterprise-grade Generative AI solutions. My work spans LLM orchestration, Retrieval-Augmented Generation (RAG), agentic/autonomous workflows, and low-latency inference architectures—helping teams turn structured and unstructured enterprise data into reliable intelligence. I’ve led end-to-end implementations across fintech, healthcare, and sales/customer intelligence, including semantic retrieval with vector databases, fine-tuning pipelines, and streaming AI systems using Kafka/Spark. I’m especially focused on MLOps/LLMOps best practices—evaluation, hallucination reduction, governance, and observability—so AI copilots and automation deliver measurable business impact at enterprise scale.…I’m Aziz Rizvi, a Lead AI/ML Engineer with 12+ years of experience building scalable data platforms, production machine learning systems, and enterprise-grade Generative AI solutions. My work spans LLM orchestration, Retrieval-Augmented Generation (RAG), agentic/autonomous workflows, and low-latency inference architectures—helping teams turn structured and unstructured enterprise data into reliable intelligence. I’ve led end-to-end implementations across fintech, healthcare, and sales/customer intelligence, including semantic retrieval with vector databases, fine-tuning pipelines, and streaming AI systems using Kafka/Spark. I’m especially focused on MLOps/LLMOps best practices—evaluation, hallucination reduction, governance, and observability—so AI copilots and automation deliver measurable business impact at enterprise scale.

Available to hire

I’m Aziz Rizvi, a Lead AI/ML Engineer with 12+ years of experience building scalable data platforms, production machine learning systems, and enterprise-grade Generative AI solutions. My work spans LLM orchestration, Retrieval-Augmented Generation (RAG), agentic/autonomous workflows, and low-latency inference architectures—helping teams turn structured and unstructured enterprise data into reliable intelligence.

I’ve led end-to-end implementations across fintech, healthcare, and sales/customer intelligence, including semantic retrieval with vector databases, fine-tuning pipelines, and streaming AI systems using Kafka/Spark. I’m especially focused on MLOps/LLMOps best practices—evaluation, hallucination reduction, governance, and observability—so AI copilots and automation deliver measurable business impact at enterprise scale.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Work Experience

Lead Gen AI Engineer at Pinecone

January 1, 2022 - Present

Architected enterprise-scale agentic AI platforms integrating LLM orchestration, autonomous workflows, and multi-agent collaboration to reduce manual lead research efforts. Designed advanced RAG pipelines using LangChain/LangGraph/LlamaIndex with Pinecone and OpenSearch, improving contextual retrieval accuracy across 15M+ indexed documents and CRM records. Built autonomous agents for prospect research, lead scoring, enrichment, qualification, and personalized outreach generation. Led enterprise migration to Generative AI ecosystems (GPT-4/4o, Claude, Gemini, Llama, Mistral), reducing campaign content generation time from hours to minutes. Engineered low-latency inference infrastructure using Kubernetes, Ray Serve, FastAPI, and vLLM with GPU acceleration. Implemented semantic search/vector retrieval to reduce hallucinations and improve grounding, and created evaluation/observability frameworks for prompt optimization, benchmarking, and AI governance. Developed fine-tuning pipelines with

Senior AI/ML Engineer at Alegion

January 1, 2018 - December 31, 2022

Led enterprise ML initiatives in predictive analytics, recommendation systems, NLP, and intelligent automation across cloud-native environments handling 120M+ records monthly. Built deep learning pipelines using TensorFlow/PyTorch/Keras for NLP and computer vision, improving document classification accuracy. Designed scalable MLOps platforms for automated training, deployment, monitoring, retraining, and governance to reduce model release cycles. Developed transformer-based NLP for sentiment analysis, entity extraction, classification, and conversational AI. Implemented feature engineering and real-time scoring systems with Spark/Kafka/Airflow, reducing streaming prediction latency. Optimized distributed training using GPU acceleration, Kubernetes, and Docker. Built CI/CD pipelines for ML deployment using Jenkins/GitHub Actions/Terraform, improving release reliability. Partnered on modernization of data lakes and analytics platforms on AWS/Azure for petabyte-scale datasets; mentored te

Software Engineer – Machine Learning at Predictif Solutions

January 1, 2015 - December 31, 2017

Developed ML solutions for forecasting, anomaly detection, customer segmentation, and predictive analytics supporting enterprise pricing and revenue optimization. Built scalable ETL and feature engineering pipelines with Python/SQL/Spark for multi-terabyte datasets. Implemented supervised and unsupervised models for classification, clustering, and recommendations to improve analytics accuracy. Designed RESTful ML services and microservices for real-time inference and batch prediction. Developed NLP pipelines with embeddings, topic modeling, tokenization, and information extraction. Operationalized models into scalable production environments with high availability and fault tolerance, using hyperparameter tuning, ensembles, and feature optimization to reduce false positives. Supported migration from monolithic analytics to cloud-native ML infrastructure using Docker and AWS services.

Associate Software Engineer – Data at Tigma Technologies

January 1, 2013 - December 31, 2014

Developed enterprise ETL and data ingestion pipelines processing millions of transactional/operational records daily across distributed analytics environments. Built data warehousing and reporting using SQL/Hadoop/Hive and relational databases to improve reporting efficiency. Automated batch workflows and data quality validation to reduce manual reconciliation. Supported modernization of distributed data infrastructure across Hadoop ecosystem technologies and scalable storage architectures. Implemented backend services and data APIs for enterprise reporting/analytics used by internal teams, improving reliability, query performance, operational monitoring, and data governance.