Results-driven DevOps/MLOps/Data Engineer with expertise in Python, SQL, Spark, Docker, Kubernetes, Terraform, Airflow, and MLflow, delivering scalable, secure, and automated cloud-native systems. Experienced in building enterprise-grade ETL pipelines, CI/CD workflows, and ML model deployments, while aligning with SaaS business models. Committed to business-focused engineering, agile execution, and high-quality outcomes.
Project - Three UK.
Developed and automated CI/CD pipelines using GitLab and Jenkins, implementing secure code reviews, automated testing, and release gates to achieve zero-downtime deployments.
Containerized and orchestrated microservices using Docker and Kubernetes, delivering scalable, resilient systems across hybrid cloud environments.
Deployed and managed cloud infrastructure as code (IaC)with Terraform and Ansible, ensuring repeatable, auditable infrastructure provisioning.
Built and optimized data models and dashboards using QuickSight and Power BI, enabling business stakeholders to access actionable product analytics.
Project - Tef Chile.
Collaborated with data scientists and ML engineers to productionize machine learning models using Airflow, MLFlow, and ONNX pipelines, supporting inferencing at scale.
Enhanced data pipeline monitoring, logging, and observability, improving SLA compliance and troubleshooting efficiency.
Participated in Agile ceremonies, delivered continuous improvements, and mentored junior team members on coding best practices, secure development, and DevOps automation.
Project - Unilever
Implemented automation scripts in Python and Bash to streamline network security, VPN access, and firewall rule configurations, reducing manual interventions.
Monitored and troubleshot infrastructure and systems in Linux environments, solving performance bottlenecks and escalating fewer tickets.
Supported product analytics and log monitoring for system health and access controls, improving visibility and compliance.
Collaborated with cross-functional teams to analyze requirements and deliver solutions that met security and performance standards.
Documented processes and assisted in developing automated toolchains, laying the foundation for continuous integration and robust DevOps practices.
Programming: Python, SQL, Java, Golang, Shell, C
Cloud: AWS, Azure, GCP, Snowflake, Databricks
DevOps/MLOps: GitLab CI/CD, Jenkins, Docker, Kubernetes, Terraform, Ansible, Airflow, MLflow
Data engineering: ETL/ELT, Spark, PySpark, data modeling, data quality, product analytics
LLM/NLP: Prompt engineering, LangChain, RAG workflows, ONNX pipelines
Systems: distributed systems, microservices, event-driven architectures
Tools: Git, Jira, Power BI, QuickSight
Soft skills: agile, collaboration, business-focused delivery, documentation
Cloud ETL pipeline: designed scalable ETL pipelines using PySpark, AWS Glue, and Redshift for high-volume data processing
CI/CD automation: built automated CI/CD pipelines with GitLab, Jenkins, Docker, and Kubernetes, reducing release cycles
ML model deployment: Deployed ML models using Airflow, MLFlow, and ONNX for scalable inferencing, aligned with business needs
Infrastructure automation: used Terraform and Ansible for IaC, ensuring secure, repeatable cloud resource provisioning
Analytics dashboards: Developed Power BI and QuickSight dashboards to monitor pipeline SLAs and business KPIs for stakeholders