Sivab

Senior AI/ML Engineer with 12+ years delivering enterprise AI, cloud analytics, and AWS-native machine learning platforms across regulated industries. Extensive experience designing secure and scalable Generative AI platforms supporting production-grade LLM applications within enterprise financial services environments. Strong expertise building enterprise Retrieval-Augmented Generation solutions using OpenAI, LangChain, vector databases, and scalable cloud-native microservices architectures.

Proven experience implementing Responsible AI frameworks supporting hallucination detection, factual accuracy validation, explainability monitoring, bias mitigation, and governance compliance. Hands-on expertise architecting AWS cloud solutions using SageMaker, Lambda, ECS, EMR, S3, API Gateway, and serverless integration frameworks. Strong background designing enterprise AI evaluation pipelines supporting model observability, output consistency validation, prompt monitoring, and production governance automation.

Extensive experience implementing distributed data engineering frameworks using Spark, Kafka, Snowflake, Delta Lake, and enterprise streaming analytics ecosystems. Proven ability designing secure REST API and microservices-based AI integration platforms supporting scalable enterprise automation and operational modernization initiatives. Experienced building cloud-native MLOps platforms supporting automated model training, deployment, monitoring, retraining, governance, and enterprise operational reliability.

Strong understanding of enterprise data standards including ingestion frameworks, warehousing architectures, secure storage patterns, and cloud governance methodologies. Hands-on expertise implementing enterprise AI security controls including IAM, RBAC, encryption standards, audit logging, and regulatory compliance frameworks. Extensive experience collaborating with Agile teams, enterprise architects, DevOps engineers, and business stakeholders delivering scalable cloud transformation programs successfully.

Skilled in designing highly available AWS analytics and AI infrastructures supporting enterprise operational resilience, scalability, and cost-efficient platform management initiatives. Proven expertise developing NLP automation, semantic search, intelligent document processing, and enterprise conversational AI applications using scalable cloud architectures. Strong communication and consulting experience recommending enterprise AWS architectures, AI modernization strategies, and scalable analytics solutions supporting business transformation goals.

Proven expertise designing secure AWS-native AI platforms supporting scalable analytics, enterprise automation, and production-grade Generative AI application environments. Extensive experience implementing enterprise microservices architectures, REST API integrations, and event-driven cloud frameworks supporting scalable AI modernization initiatives. Strong background developing cost-efficient d

Senior AI/ML Engineer – Generative AI at Fifth Third Bank (2024-06 – Present)

Designed and implemented enterprise Generative AI, machine learning, and AWS-native analytics platforms supporting banking modernization, fraud prevention, intelligent automation, and enterprise decision-support initiatives. Built secure cloud-native AI ecosystems leveraging scalable data engineering, MLOps automation, streaming analytics, and Responsible AI governance frameworks supporting highly regulated financial services environments.

• Architected enterprise Generative AI and Retrieval-Augmented Generation platforms using OpenAI, LangChain, Pinecone, and scalable AWS-native cloud services.
• Designed secure AWS AI infrastructure using SageMaker, ECS, Lambda, API Gateway, Kubernetes, and Terraform supporting enterprise machine learning workloads.
• Developed enterprise AI evaluation frameworks supporting hallucination detection, factual accuracy validation, output consistency monitoring, and Responsible AI governance controls.
• Implemented explainability, bias monitoring, transparency validation, and audit governance frameworks supporting regulated banking AI compliance requirements effectively.
• Engineered scalable REST APIs and microservices architectures supporting enterprise AI integrations, semantic search platforms, and intelligent banking automation services.
• Built distributed data engineering pipelines using Spark, Kafka, Snowflake, Delta Lake, and Airflow processing enterprise financial analytics workloads efficiently.
• Developed fraud detection and risk analytics models using PySpark, XGBoost, Scikit-learn, and streaming analytics frameworks supporting banking operations.
• Built enterprise MLOps automation pipelines using MLflow, Jenkins, Docker, Kubernetes, and Terraform reducing production deployment and monitoring overhead significantly.
• Implemented real-time streaming analytics solutions using Kafka, Spark Structured Streaming, and Databricks supporting low-latency banking event processing capabilities.
• Developed NLP-driven intelligent document processing and workflow automation solutions using Hugging Face Transformers, OCR, and enterprise AI integration services.
• Optimized distributed Spark and Delta Lake processing workloads improving enterprise analytics scalability, ingestion performance, and operational reporting reliability.
• Implemented enterprise security governance controls including IAM, RBAC, encryption standards, audit logging, and PII masking supporting regulatory compliance requirements.
• Collaborated closely with enterprise architects, DevOps engineers, cybersecurity teams, and Agile stakeholders delivering scalable AWS AI modernization initiatives.
• Built executive reporting dashboards using Power BI, Tableau, and Streamlit supporting operational KPI monitoring and enterprise decision-support analytics initiatives.

Senior Machine Learning Engineer at CVS Health (2022-11 – 2024-05)

Developed enterprise AI, predictive analytics, and intelligent automation platforms supporting healthcare operations, fraud analytics, claims modernization, and customer intelligence initiatives. Implemented scalable AWS architectures, distributed data engineering frameworks, NLP-driven automation platforms, and secure MLOps governance solutions supporting enterprise healthcare analytics and decision-support systems.

• Architected scalable AWS-native AI platforms supporting predictive analytics, intelligent claims automation, and enterprise operational decision-support capabilities.
• Developed advanced machine learning models using XGBoost, LightGBM, Spark ML, and Scikit-learn improving enterprise analytics and forecasting accuracy.
• Designed enterprise REST API and microservices architectures supporting secure AI integrations, claims processing workflows, and cloud-native healthcare applications.
• Built secure MLOps automation frameworks using SageMaker, MLflow, Kubernetes, Docker, Jenkins, and Terraform supporting scalable production AI deployments.
• Implemented enterprise AI governance controls supporting explainability monitoring, output validation, bias reduction, and operational transparency across AI platforms.
• Developed NLP-driven intelligent document processing and automation solutions using Hugging Face Transformers, OCR, OpenAI APIs, and enterprise AI integrations.
• Built distributed ETL and streaming analytics pipelines using Spark, Kafka, Snowflake, Airflow, and Delta Lake supporting enterprise healthcare reporting ecosystems.
• Developed anomaly detection and fraud analytics frameworks using Python, PySpark, Random Forest, and predictive machine learning methodologies effectively.
• Built semantic search and conversational AI solutions using LangChain, vector embeddings, Pinecone, and scalable enterprise cloud integration services.