About the Role
We are seeking a highly experienced Senior AWS DevOps Engineer to lead the design, automation, and scaling of our cloud infrastructure. This role is ideal for a DevOps professional with a strong background in AWS, serverless computing, and infrastructure automation—and a keen interest or experience in supporting machine learning and generative AI workloads in production.
Key Responsibilities
Architect and maintain robust, scalable AWS infrastructure for production, development, and testing environments.
Automate infrastructure using Terraform, CloudFormation, and CDK.
Build and manage CI/CD pipelines for deploying microservices, serverless applications, and ML models.
Design and manage serverless architectures using AWS Lambda, API Gateway, Step Functions, EventBridge, and DynamoDB.
Support and optimize ML workflows and pipelines using tools such as SageMaker, ECS, or EKS.
Collaborate with data scientists and ML engineers to productionize and monitor generative AI models and pipelines.
Implement observability tools for performance monitoring, logging, and alerting (e.g., CloudWatch, Prometheus, Grafana, Datadog).
Manage secure IAM policies, VPC configurations, and secrets across environments.
Drive cost optimization and infrastructure efficiency across AWS services.
Mentor junior engineers and contribute to DevOps best practices across the organization.
Required Skills and Experience
5+ years of experience in DevOps, SRE, or Cloud Infrastructure roles.
Deep expertise with AWS (Lambda, ECS/EKS, S3, IAM, CloudWatch, CloudFront, etc.).
Proven experience designing and operating serverless AWS infrastructure.
Strong proficiency in Infrastructure as Code (Terraform, CloudFormation, or CDK).
Experience with CI/CD pipelines (GitHub Actions, GitLab CI, CircleCI, etc.).
Hands-on experience supporting machine learning or AI workloads in production.
Familiarity with SageMaker, Amazon Bedrock, or deploying custom generative AI models.
Strong scripting skills (e.g., Python, Bash).
Knowledge of container orchestration (Kubernetes, EKS) and Docker.
Preferred Qualifications
AWS Certified DevOps Engineer or AWS Certified Machine Learning Specialty.
Experience with generative AI frameworks (e.g., Hugging Face Transformers, LangChain, OpenAI APIs).
Understanding of MLOps tools (MLflow, Weights & Biases, etc.).
Familiarity with event-driven architectures and asynchronous systems.
Experience in regulated environments (e.g., HIPAA, SOC 2, GDPR).What We Offer
Competitive salary and equity options
Flexible remote-first working environment
Access to cutting-edge AI/ML projects
Professional development and certification budget
Health insurance and wellness perks
The opportunity to shape and scale modern cloud infrastructure at a tech-forward company