- Lead and oversee multiple AWS-based delivery initiatives in parallel
- Act as the primary technical point of contact for client stakeholders
- Architect and guide cloud solutions leveraging AWS best practices
- Mentor and enable offshore teams, ensuring progress during distributed working hours
- Proactively identify risks, dependencies, and bottlenecks; unblock teams effectively
- Drive infrastructure automation and platform maturity
- Collaborate with AI/ML engineers to support ML infrastructure, pipelines, and deployments
- Ensure security, reliability, scalability, and cost-awareness across cloud workloads
Required Skills Experience
- 5+ years of hands-on experience delivering AWS projects, with overall 10+ years of experience
- Deep understanding of core AWS services (IAM, VPC, ECS/EKS, Lambda, S3, RDS, Sagemaker etc.)
- Strong expertise in container technologies (Docker) and container orchestration (Kubernetes/EKS)
- Proven experience with Infrastructure as Code (AWS CDK preferred; Terraform acceptable)
- Hands-on experience with GitHub Actions and CI/CD workflow automation
- Understanding of the AI/ML ecosystem, including model deployment and ML infrastructure patterns
- Experience owning client communication, technical discussions, and delivery updates
- Ability to support and unblock teams during offshore working hours
- Self-driven, proactive, and accountable with a robust ownership mindset
Nice to Have
- Experience with MLOps platforms and ML pipelines
- Exposure to cloud cost optimization and FinOps practices
- Prior people-management or delivery-lead experience
Mandatory Competencies
- Data AI - MLOPS - CI/CD (for ML pipelines)
- Data AI - MLOPS - Data Pipeline Feature Management
- Data AI - MLOPS - Model Registry Experiment Tracking
- DevOps/Configuration Mgmt - DevOps/Configuration Mgmt - Containerization (Docker, Kubernetes)
- Data AI - MLOPS - Python
- Data AI - MLOPS - Machine Learning (ML)