Introduction
A Data + ML Infrastructure Engineer plays a critical role in designing, building, and maintaining the systems that support machine learning workflows and large-scale data processing. In 2026, having a well-structured, ATS-optimized resume is essential to stand out in competitive job markets, especially as companies increasingly prioritize automation and scalable data solutions. This guide provides practical advice for creating a resume tailored to this role, focusing on keyword integration, clarity, and industry relevance.
Who Is This For?
This guide is for professionals with mid-level to advanced experience in data engineering, machine learning infrastructure, or related fields. It applies broadly to those in the USA, UK, Canada, Australia, Germany, Singapore, or similar regions. Whether you are switching careers, returning after a break, or seeking a higher position, the principles here help craft a resume that aligns with the expectations of hiring managers and ATS systems alike.
Resume Format for Data + ML Infrastructure Engineer (2026)
The ideal resume for this role typically starts with a clear Summary or Profile section highlighting your core expertise. Follow with Skills tailored to data and ML infrastructure, then detail your Professional Experience with impactful, metrics-based bullets. Adding a Projects or Portfolio section can be beneficial if you have relevant open-source contributions, personal projects, or certifications. Finish with Education and optional Certifications.
Use a two-page format if you possess extensive experience, especially in specialized areas. For most mid-career professionals, a one-page resume emphasizing key skills and achievements suffices. Ensure your Skills and Experience sections are keyword-rich to pass ATS scans. For roles heavily focused on portfolio work or certifications, include a link to your online profile or GitHub.
Role-Specific Skills & Keywords
- Cloud platforms: AWS, GCP, Azure, Databricks
- Data pipelines: Apache Spark, Kafka, Airflow, Beam
- Machine Learning frameworks: TensorFlow, PyTorch, scikit-learn
- Infrastructure tools: Kubernetes, Docker, Terraform, Helm
- Data storage: Snowflake, Redshift, Hadoop, HDFS
- DevOps practices: CI/CD pipelines, Jenkins, Git
- Monitoring: Prometheus, Grafana, ELK Stack
- Programming: Python, Scala, Java, Bash scripting
- Data security & governance: GDPR, IAM, encryption
- Automation & orchestration: Airflow, Kubeflow
- Soft skills: Collaboration, problem-solving, communication
In 2026, ATS systems also prioritize familiarity with emerging tech like MLops tools, real-time data streaming, and AI model deployment pipelines.
Experience Bullets That Stand Out
- Developed scalable data pipelines using Apache Spark and Kafka, reducing data processing time by ~20%, enabling faster model training cycles.
- Designed and implemented cloud-native ML infrastructure on AWS, supporting deployment of over 50 ML models with 99.9% uptime.
- Automated CI/CD workflows for ML models using Jenkins and Terraform, decreasing deployment time by ~30% and minimizing manual errors.
- Managed containerized ML environments with Docker and Kubernetes, facilitating seamless model testing and scaling across multiple regions.
- Collaborated with data scientists to optimize data storage solutions, resulting in 15% cost savings while improving data access latency.
- Led migration of legacy data systems to cloud platforms, ensuring compliance with GDPR and enhancing data security protocols.
- Monitored system health using Prometheus and Grafana, achieving proactive issue detection and reducing downtime by ~10%.
Common Mistakes (and Fixes)
- Vague summaries: Instead, specify your expertise and key achievements in the summary, e.g., “Experienced Data + ML Infrastructure Engineer with a track record of building scalable pipelines on AWS.”
- Dense paragraphs: Break information into bullet points; use clear, action-oriented statements.
- Overuse of generic skills: Tailor skills to the role; avoid listing skills without context or relevance.
- Decorative formatting: Use simple, ATS-friendly fonts and avoid tables or text boxes that can disrupt parsing.
- Lack of metrics: Quantify achievements wherever possible to demonstrate impact.
ATS Tips You Shouldn't Skip
- Save your resume as a Word document (.docx) or plain PDF, following employer guidelines.
- Use clear section labels: Summary, Skills, Experience, Projects, Education.
- Incorporate synonyms for keywords, e.g., “data pipelines” and “ETL workflows,” to improve ATS matching.
- Keep formatting simple: avoid headers and footers, graphics, or complex tables.
- Maintain consistent tense: past tense for previous roles, present tense for current role.
- Use bullet points for experience descriptions to enhance scanability.
- Name your file with your full name and role, e.g.,
John_Doe_Data_ML_Infra_2026.docx.
Following these guidelines will help your resume navigate ATS filters effectively and put your qualifications in front of hiring managers.