DevOps Engineer
Syntas is hiring a DevOps Engineer to build and maintain the deployment pipelines, infrastructure automation, and operational tooling that enable our clients to ship software reliably and frequently. You will implement CI/CD systems, containerization strategies, and infrastructure as code that transform how engineering teams deliver value—with particular focus on the unique requirements of AI/ML workloads. This role is ideal for engineers who love automating away manual processes, building self-service platforms, and creating the reliable foundations that let development teams move fast without breaking things. You will work across diverse client environments, bringing modern DevOps practices to organizations at various stages of maturity.
About the Role
As a DevOps Engineer at Syntas, you will be the engineer who makes deployments boring—in the best possible way. Your goal is to create systems where shipping code is routine, infrastructure changes are safe and reversible, and operations teams sleep through the night because automation handles the chaos. You will build the pipelines, platforms, and practices that turn software delivery from a stressful event into a non-event.
Your work spans the full DevOps lifecycle. On the CI side, you will design build systems that compile, test, and package applications efficiently. On the CD side, you will create deployment pipelines that move code through environments with appropriate gates, approvals, and rollback capabilities. For infrastructure, you will implement IaC practices that make cloud resources as manageable as application code. For operations, you will build observability systems that provide visibility into production health and accelerate incident response.
The AI/ML dimension adds interesting complexity to this role. ML models have different deployment patterns than traditional applications—you will implement model serving infrastructure, handle large artifact storage and versioning, create A/B testing frameworks for model evaluation, and build pipelines that span data preparation through model deployment. Understanding the ML lifecycle will differentiate your work from generic DevOps implementations.
We value DevOps engineers who think about developer experience, not just operational metrics. The best DevOps implementations are the ones developers love using—fast feedback loops, clear error messages, self-service capabilities, and documentation that actually helps. You should be passionate about reducing friction and creating tools that make other engineers more productive.
What You Will Build
- 1CI/CD pipelines that take code from commit to production with appropriate testing and approval gates
- 2Infrastructure as code implementations using Terraform or Pulumi for reproducible cloud deployments
- 3Kubernetes platforms with GitOps workflows for declarative, auditable infrastructure management
- 4ML deployment pipelines that handle model versioning, serving, and A/B testing
- 5Observability stacks with metrics, logging, and tracing for production visibility
- 6Self-service platforms that let developers provision resources without DevOps bottlenecks
- 7Disaster recovery automation including backup systems, failover testing, and runbook automation
Key Responsibilities
- Design and implement CI/CD pipelines for diverse application types including web apps, APIs, and ML models
- Build and maintain Kubernetes clusters with appropriate networking, storage, and security configurations
- Implement infrastructure as code using Terraform, Pulumi, or similar tools for cloud resource management
- Create GitOps workflows using ArgoCD, Flux, or similar tools for declarative infrastructure management
- Deploy and configure observability tools including Prometheus, Grafana, and distributed tracing systems
- Implement ML deployment patterns including model serving, versioning, and canary releases
- Build container images and optimize Docker workflows for fast builds and small image sizes
- Create automated testing frameworks for infrastructure including integration and chaos testing
- Implement security practices including secrets management, vulnerability scanning, and policy enforcement
- Develop runbooks and automation for common operational tasks and incident response
- Collaborate with development teams to improve build times, deployment frequency, and reliability
- Document DevOps practices, create onboarding materials, and train client teams
- Stay current with DevOps tools and practices, evaluating new technologies for potential adoption
What We Are Looking For
- 4+ years of DevOps, SRE, or infrastructure engineering experience
- Strong experience with CI/CD platforms (GitHub Actions, GitLab CI, Jenkins, CircleCI)
- Proficiency with infrastructure as code tools, particularly Terraform
- Hands-on Kubernetes experience including deployment, networking, and troubleshooting
- Experience with containerization including Docker, image optimization, and registry management
- Strong scripting skills in Python, Bash, or Go for automation and tooling
- Experience with at least one major cloud platform (AWS, GCP, or Azure)
- Knowledge of observability tools: Prometheus, Grafana, ELK stack, or Datadog
- Understanding of GitOps principles and tools (ArgoCD, Flux)
- Experience with secrets management (Vault, AWS Secrets Manager, or similar)
- Strong troubleshooting skills across applications, infrastructure, and networking
- Excellent communication skills for working with development teams and documenting practices
Nice to Have
- Experience with ML deployment platforms (MLflow, Kubeflow, Seldon)
- Background in platform engineering and internal developer platforms
- Knowledge of service mesh technologies (Istio, Linkerd)
- Experience with FinOps and cloud cost optimization
- Familiarity with compliance frameworks and security automation
- Background in SRE practices including SLOs, error budgets, and incident management
- Experience with database operations and migration automation
- Knowledge of networking: DNS, load balancing, CDNs, and traffic management
- Prior consulting experience with diverse client environments
- Contributions to open source DevOps or cloud-native projects
- Certifications: CKA, AWS DevOps Professional, or similar
- Experience with chaos engineering tools (Chaos Monkey, Litmus)
Tech Stack
Benefits & Perks
- Competitive salary: $130,000 - $180,000 depending on experience
- Equity participation with meaningful upside as we grow
- Fully remote work with flexible hours—work from anywhere in the US
- Comprehensive health, dental, and vision insurance (100% premium covered for employee)
- Unlimited PTO with encouraged minimum of 4 weeks—we mean it
- $3,000 annual learning and development budget for courses, books, and certifications
- Conference attendance budget including travel—attend or speak at KubeCon and other DevOps conferences
- Top-tier hardware: MacBook Pro, external display, and peripherals of your choice
- All AI tools and subscriptions you need: GPT-4, Claude, GitHub Copilot, and more
- Quarterly team offsites in interesting locations
- 401(k) with company match
- Paid parental leave (12 weeks)
- Home office setup stipend ($1,000)
- Work on genuinely interesting problems across diverse industries
Ready to Apply?
Send us your resume and a brief introduction. Tell us about your experience with AI/ML systems and what excites you about this opportunity.