Devops Engineer

As a Senior DevOps Engineer at Hatch, your key responsibilities include designing and evolving our cloud infrastructure primarily on AWS using infrastructure-as-code tools. You will collaborate closely with ML engineers to manage the lifecycle of machine learning models, ensuring they are production-ready and efficiently running across all stages. Additionally, you'll drive strategies for observability, logging, and incident response while mentoring other engineers and enhancing the development lifecycle by implementing DevOps best practices.

To qualify for the Senior DevOps Engineer role at Hatch, candidates should have a minimum of 7 years of experience in DevOps, SRE, or platform engineering, preferably in high-growth environments. You should demonstrate deep expertise in AWS infrastructure and services, experience with infrastructure-as-code tools like Terraform, and CI/CD tooling. Additionally, knowledge of machine learning platforms and strong skills in container orchestration and observability stacks are crucial. You should also be adept at leading architectural discussions for large-scale infrastructure improvements.

The DevOps team at Hatch plays a crucial role in supporting machine learning initiatives by designing infrastructure that facilitates seamless versioning, orchestration, and monitoring of ML models. This includes optimizing data pipelines and model serving infrastructure for performance and collaborating with ML engineers to productionize models. You’ll work within an infrastructure suited for data processing and ensure the environments are scalable, secure, and cost-efficient across development and production stages.

Senior DevOps Engineers at Hatch frequently utilize a range of tools to ensure effective management of infrastructure. This includes infrastructure-as-code tools like Terraform or Pulumi, CI/CD tools such as GitHub Actions or ArgoCD, and observability stacks like Datadog, Prometheus, and Grafana. Familiarity with tools for machine learning workflows such as MLflow and SageMaker is also essential. These tools enhance efficiency, enabling the development and deployment of reliable systems.

The working culture at Hatch for a Senior DevOps Engineer is dynamic, collaborative, and focused on delivering exceptional AI products. Engineers take ownership of their work within a supportive environment that values technical leadership, fast decision-making, and curiosity. You’ll find yourself working alongside highly skilled engineers and product leaders, fostering a sense of camaraderie while pushing boundaries in infrastructure and machine learning technologies. It’s a place where your ideas are valued and innovation thrives.

Certainly! When discussing my experience with AWS services, I would emphasize my hands-on activities such as managing EC2 instances, configuring VPCs for secure networking, utilizing IAM for access management, and deploying applications using ECS or EKS. I’d also mention my proficiency in setting up serverless architectures using AWS Lambda and how these experiences align with driving efficiencies and scalability for cloud infrastructure in my previous roles.

My approach to implementing infrastructure as code primarily involves using tools like Terraform or Pulumi. I would describe my process as beginning with thorough planning to identify the required infrastructure components, writing clear modular code, and utilizing version control for tracking changes. Furthermore, testing the code in isolated environments ensures reliability before deploying to production. I also believe in documenting the infrastructure code for team collaboration and knowledge sharing.

To ensure reliability and scalability, I focus on designing systems that are both resilient and redundant, leveraging auto-scaling features to handle varying loads. Regularly implementing monitoring and alerting, using tools like Datadog and Grafana, helps catch issues proactively. Additionally, performing incident response drills and thorough root cause analysis after outages informs continuous improvement strategies that enhance overall system performance and reliability.

I believe DevOps plays a crucial role in the machine learning lifecycle by bridging the gap between development, data science, and operations. It ensures smooth transitions from model training to production deployment, advocating for best practices in CI/CD to automate this pipeline. This not only speeds up development but also guarantees that models are consistently monitored and maintained in a scalable and effective manner, enabling ongoing performance analysis and model updates.

Absolutely! One challenging incident involved a major application outage due to resource constraints. I led the troubleshooting process, which included analyzing logs and system metrics. After identifying the bottleneck in our network settings, I collaborated with the team to reconfigure our infrastructure, which not only restored services quickly but also improved our capacity planning. Post-incident analysis drove enhancements to our monitoring and alerting strategy as well.

I utilize a combination of real-time monitoring tools, such as Prometheus and Grafana, and logging services, like ELK stack, to create a comprehensive monitoring strategy. I set up dashboards to visualize key metrics and alerts for performance degradation or outages. Regularly reviewing these metrics with the team fosters continuous improvement, allowing us to optimize system performance and reliability proactively.

I take a hands-on approach when mentoring junior engineers by encouraging them to participate in real projects alongside me. I focus on explaining the rationale behind DevOps practices thoroughly, allowing them to ask questions freely. Additionally, I recommend resources and guide them through troubleshooting processes, which fosters a collaborative learning environment and helps accelerate their skills for handling infrastructure challenges.

In managing security for cloud infrastructure, I prioritize network segmentation, using VPCs and security groups to minimize exposure. I implement strict IAM roles and policies to ensure principle of least privilege. Regular audits using tools and continuous vulnerability assessments are critical. Additionally, employing CloudTrail for logging and establishing a robust incident response plan are also key components of my security best practices strategy.

My experience with CI/CD pipelines involves implementing automated workflows using tools like GitHub Actions and Jenkins. I focus on defining clear stages, such as build, test, and deployment phases, ensuring each application iteration is thoroughly validated before going live. This oversight in creating CI/CD processes allows for faster but more reliable releases, significantly accelerating our release cycle while managing quality standards effectively.

Collaboration is vital in the role of a Senior DevOps Engineer. It involves working closely with engineering, development, and product teams to ensure alignment on goals, best practices, and tool usage. Regular stand-ups, feedback loops, and cross-functional meetings facilitate seamless integration of DevOps practices into the development lifecycle and foster a culture of shared responsibility for system reliability and performance.