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AI/ML engineer with hands-on experience building AI/ML, cloud, and infrastructure systems across Linux, AWS, Docker, Kubernetes, Terraform, Ansible, Prometheus, Grafana, Jenkins, and Elasticsearch. Built end-to-end solutions including a multimodal biometric security platform and containerized AI workloads exposed via FastAPI. Worked on AI-powered incident response and an MLOps/model lifecycle platform featuring experiment tracking, model versioning, FastAPI serving, drift detection, automated retraining/validation, and CI/CD with strong latency and performance targets.…AI/ML engineer with hands-on experience building AI/ML, cloud, and infrastructure systems across Linux, AWS, Docker, Kubernetes, Terraform, Ansible, Prometheus, Grafana, Jenkins, and Elasticsearch. Built end-to-end solutions including a multimodal biometric security platform and containerized AI workloads exposed via FastAPI. Worked on AI-powered incident response and an MLOps/model lifecycle platform featuring experiment tracking, model versioning, FastAPI serving, drift detection, automated retraining/validation, and CI/CD with strong latency and performance targets.

Awon Aziz

AI Engineer, Software Engineer, Data Scientist, +2

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AI/ML engineer with hands-on experience building AI/ML, cloud, and infrastructure systems across Linux, AWS, Docker, Kubernetes, Terraform, Ansible, Prometheus, Grafana, Jenkins, and Elasticsearch. Built end-to-end solutions including a multimodal biometric security platform and containerized AI workloads exposed via FastAPI. Worked on AI-powered incident response and an MLOps/model lifecycle platform featuring experiment tracking, model versioning, FastAPI serving, drift detection, automated retraining/validation, and CI/CD with strong latency and performance targets.…AI/ML engineer with hands-on experience building AI/ML, cloud, and infrastructure systems across Linux, AWS, Docker, Kubernetes, Terraform, Ansible, Prometheus, Grafana, Jenkins, and Elasticsearch. Built end-to-end solutions including a multimodal biometric security platform and containerized AI workloads exposed via FastAPI. Worked on AI-powered incident response and an MLOps/model lifecycle platform featuring experiment tracking, model versioning, FastAPI serving, drift detection, automated retraining/validation, and CI/CD with strong latency and performance targets.

Available to hire

AI/ML engineer with hands-on experience building AI/ML, cloud, and infrastructure systems across Linux, AWS, Docker, Kubernetes, Terraform, Ansible, Prometheus, Grafana, Jenkins, and Elasticsearch. Built end-to-end solutions including a multimodal biometric security platform and containerized AI workloads exposed via FastAPI.

Worked on AI-powered incident response and an MLOps/model lifecycle platform featuring experiment tracking, model versioning, FastAPI serving, drift detection, automated retraining/validation, and CI/CD with strong latency and performance targets.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Beginner

Language

English

Advanced

Work Experience

ML Model Lifecycle & Drift Detection Platform (project)

January 1, 2026 - January 1, 2026

Built an end-to-end MLOps platform using MLflow experiment tracking, model versioning, FastAPI serving, drift detection, GitHub Actions retraining, and a Streamlit model-health dashboard. Implemented PSI, Kolmogorov-Smirnov, and Jensen-Shannon drift checks; automatically retrains a challenger model and promotes it only when it outperforms the champion. Added prediction logging, latency/metrics endpoints, CI/CD workflows, Docker deployment, and automated tests; achieved <5 ms p99 API prediction latency and ~2-minute CI/CD execution time in evaluation.

AI-Powered Multi-Cloud Incident Response System (project)

January 1, 2026 - January 1, 2026

Simulated a multi-cloud AI Ops system across AWS, Azure, and GCP that ingests infrastructure metrics, detects anomalies using Isolation Forest, triages incidents, and exposes a live terminal dashboard. Implemented severity scoring, root-cause hints, incident deduplication, SLA tracking, and pluggable notifications via Slack, PagerDuty, email, and webhooks. Evaluated performance with <200 ms average triage time and <8% false-positive rate. Containerized the system with Docker and exposed services through FastAPI with automated tests and reproducible run instructions.

AI/ML Engineering Intern (AI Ops Engineering Intern) at Al Nafi Cloud

January 1, 2024 - January 1, 2026

Built hands-on AI, ML, cloud, and infrastructure systems as part of an AI Ops program using RHEL/Linux, AWS, Docker, Kubernetes, Terraform, Ansible, Prometheus, Grafana, Jenkins, Elasticsearch, ML/deep learning, and NLP. Developed and presented an end-to-end multimodal biometrics security platform covering biometric authentication, privacy-preserving templates, behavioral analytics, and Zero-Trust security. Built and troubleshot containerized workloads using Docker/Podman and Docker Compose/Podman Compose including image creation, networking, volumes, secrets, logging, debugging, and multi-container applications.