Introduction
An ATS-friendly resume for an ML Infrastructure Engineer in 2026 focuses on highlighting technical expertise, project experience, and problem-solving skills specific to machine learning systems. As AI and data-driven applications grow, recruiters seek candidates who can build scalable, reliable infrastructure to support machine learning models. Crafting a clear, keyword-rich resume ensures your application makes it through automated filters and captures the attention of hiring managers.
Who Is This For?
This guide is suited for professionals with mid-level to advanced experience in ML infrastructure, including those transitioning from related roles like DevOps, data engineering, or software engineering. It applies across regions such as the USA, UK, Canada, Australia, Germany, and Singapore, where AI tech development remains robust. Whether you're an experienced engineer aiming for a senior role or a specialist reorienting your career, this advice helps you present your skills effectively. If you're returning to the workforce or switching jobs, emphasizing transferable skills and relevant projects is crucial.
Resume Format for ML Infrastructure Engineer (2026)
Use a clean, ATS-compatible structure: start with a Summary or Profile, followed by Skills, Experience, Projects, Education, and Certifications. Prioritize clarity and relevance, organizing information logically. For those with extensive experience, a two-page resume is acceptable, but ensure the most critical details appear early. Including a Projects or Portfolio section can showcase hands-on work, especially for roles emphasizing practical deployment or open-source contributions. Keep formatting simple: avoid tables, text boxes, or overly decorative elements that may disrupt ATS parsing.
Role-Specific Skills & Keywords
- Cloud platforms: AWS, GCP, Azure, IBM Cloud
- Containerization & orchestration: Docker, Kubernetes, OpenShift
- Infrastructure-as-Code: Terraform, CloudFormation, Ansible
- Machine learning frameworks: TensorFlow, PyTorch, JAX
- Data pipelines: Apache Airflow, Kafka, Spark
- Model deployment: TensorFlow Serving, TorchServe, ONNX
- CI/CD pipelines: Jenkins, GitLab CI, CircleCI
- Monitoring & Logging: Prometheus, Grafana, ELK Stack, Datadog
- Programming languages: Python, Bash, Go, Java
- Version control: Git, SVN
- Security & compliance: IAM, encryption, GDPR, SOC standards
- Soft skills: Collaboration, problem-solving, adaptability, communication
In 2026, ATS scans also look for keywords related to scalable infrastructure, automation, and continuous deployment in ML contexts. Use synonyms like "model deployment," "data pipeline automation," and "cloud orchestration" to diversify keyword usage.
Experience Bullets That Stand Out
- Designed and implemented scalable ML pipelines on AWS, reducing training time by ~20% through optimized resource provisioning.
- Automated model deployment workflows using Kubernetes and CI/CD tools, resulting in faster iteration cycles and improved reliability.
- Led migration of legacy ML infrastructure to cloud-native solutions, decreasing operational costs by ~15%.
- Developed monitoring dashboards with Prometheus and Grafana, enabling proactive issue detection and reducing downtime by ~10%.
- Collaborated with data scientists and software engineers to integrate ML models into production environments, ensuring compliance with security standards.
- Managed infrastructure-as-code projects using Terraform and Ansible, streamlining environment setup and configuration management.
- Conducted performance tuning and stress testing of ML systems, supporting high-volume inference with minimal latency.
Common Mistakes (and Fixes)
- Vague summaries: Avoid generic descriptions like “experienced in ML infrastructure.” Instead, specify tools, projects, and outcomes.
- Overloaded paragraphs: Break down complex responsibilities into clear, bullet-pointed achievements or tasks.
- Lack of keywords: Incorporate role-specific terms naturally within experience descriptions; don’t stuff keywords unnaturally.
- Inconsistent formatting: Use uniform bullet styles, font sizes, and tense (preferably past tense for past roles).
- Heavy graphics or tables: Keep layout simple to ensure ATS readability; avoid decorative elements that can break parsing.
ATS Tips You Shouldn't Skip
- Save your resume as a Word document (.docx) or PDF, depending on employer preferences, but ensure compatibility.
- Use clear section headings like "Experience," "Skills," and "Projects" with standard fonts.
- Name your file with your full name and role, e.g.,
Jane_Doe_ML_Infrastructure_Engineer_2026. - Incorporate common synonyms for key skills, e.g., "model deployment" and "ML deployment."
- Maintain consistent tense: past tense for previous roles, present tense for current responsibilities.
- Avoid excessive abbreviations unless widely recognized (e.g., AWS, GCP).
- Use bullet points to improve scanability; limit each to 1-2 lines for clarity.
- Regularly review the job description to tailor your keywords accordingly, matching the language used by the employer.