AI/ML Engineer with experience designing, developing, and deploying intelligent software solutions using machine learning, deep learning, and Large Language Models (LLMs). Skilled in building NLP applications, RAG pipeli…
AI/ML Engineer with experience designing, developing, and deploying intelligent software solutions using machine learning, deep learning, and Large Language Models (LLMs). Skilled in building NLP applications, RAG pipelines, model fine-tuning, prompt engineering, and scalable AI workflows using Python and modern ML frameworks. Experienced in translating business problems into production-ready AI systems, integrating APIs, vector databases, and cloud technologies to deliver measurable impact. Passionate about building practical, user-focused AI products that improve efficiency and decision-making.
<p>Orchestrated agent-based architectures in backend systems engineering to facilitate agentic workflows, resulting in improved system observability and a 99.95% reliability rate across distributed services during peak usage.<br>
Engineered large language model (LLM) powered services within production backend distributed systems, enabling scalable AI features that optimized performance and reduced latency across multiple microservices, resulting in a 30% increase in throughput.<br>
Orchestrated HIPAA-compliant ingestion pipelines in AWS S3, Glue, and Airflow, delivering curated clinical features to SageMaker training jobs and dependable patient-risk predictions at scale.<br>
Deployed containerized inference services with Docker and Kubernetes, integrating REST APIs and API Gateway, ensuring low-latency scoring for care-management applications and audits securely today.<br>
Validated model performance with A/B testing, MLflow tracking, and bias checks, improving governance readiness and enabling clinicians to trust model recommendations during triage consistently.<br>
Instrumented end-to-end monitoring with CloudWatch, Prometheus, and alerting runbooks, reducing production incidents, accelerating recovery, and safeguarding SLA commitments for critical analytics consumers every release.<br>
Standardized feature store practices with Spark, Parquet, and SQL, minimizing data drift, improving lineage, and accelerating reproducible experimentation for new cohort-stratification models across teams.</p>
Orchestrated agent-based architectures in backend systems engineering, Engineered large language model (LLM) powered services, Orchestrated HIPAA-compliant ingestion pipelines, Deployed containerized inference services, Validated model performance with A/B testing, Instrumented end-to-end monitoring, Standardized feature store practices
<p>Spearheaded deployment challenges by creating evaluation frameworks that streamlined cost management processes, delivering a tangible impact through a 25% reduction in operational expenses and improving deployment success rates significantly.<br>
Engineered fraud-detection pipelines with Kafka, Spark Streaming, and Python, transforming event data into real-time features and reliable anomaly alerts for investigators daily at scale.<br>
Modernized MLOps workflows with GitHub Actions, Terraform, and Kubernetes, enabling repeatable deployments, separation-of-duties controls, auditable rollbacks, consistent promotion, and faster approvals across environments securely.<br>
Analyzed model explainability with SHAP, governance documentation, and lineage tooling, supporting model risk reviews and improving transparency for compliance stakeholders and business partners proactively.<br>
Secured data access with IAM, KMS, and VPC endpoints, protecting sensitive banking datasets and enabling controlled experimentation without operational interruptions across teams enterprise-wide continuously.<br>
Optimized batch scoring jobs on AWS SageMaker and EMR, reducing compute waste, improving job reliability, and delivering timely portfolio insights to downstream reporting systems.</p>
Spearheaded deployment challenges by creating evaluation frameworks, Engineered fraud-detection pipelines, Modernized MLOps workflows, Analyzed model explainability, Secured data access, Optimized batch scoring jobs
<p>Modernized AI systems using modern systems programming languages to achieve substantial latency reduction, enhancing user experience through optimized algorithms that processed requests 40% faster in high-load scenarios.<br>
Integrated claims and eligibility datasets with Snowflake, dbt, and SQL, producing trusted analytics marts that accelerated risk-adjustment model development and validation across programs nationwide.<br>
Automated retraining triggers with Airflow, Feature Store, and model registry, keeping deployed models current, reducing drift, and improving consistency across member populations reliably monthly.<br>
Refactored NLP pipelines with Transformers and PyTorch, extracting clinical concepts from notes and strengthening downstream decision-support workflows for care teams daily at scale enterprise-wide.<br>
Configured retrieval-augmented generation with AWS Bedrock, LangChain, and OpenSearch, enabling secure question answering over policy content and operational procedures for agents securely, compliant, scalable.<br>
Governed experiment tracking with MLflow, DVC, and reproducible notebooks, improving collaboration, auditability, and handoffs between data science and engineering squads across releases end-to-end consistently.</p>
Modernized AI systems using modern systems programming languages, Integrated claims and eligibility datasets, Automated retraining triggers, Refactored NLP pipelines, Configured retrieval-augmented generation, Governed experiment tracking
نظام التوظيف التشغيلي المعتمد على الذكاء الاصطناعي أولاً، الذي يوحّد ويؤتمت دورة التوظيف بالكامل للمؤسسات الحديثة حول العالم.
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