Duration: 12 months + extensions
Location: Remote (Occasional Travel to Poland)
Languages: English Fluent (Polish a plus)
B2B Freelance
Full-Time
Job Role:
We are seeking a highly skilled and experienced Expert Cloud Engineer specializing in Google Cloud Platform (GCP) and VertexAI to join our dynamic team. The ideal candidate will possess extensive expertise in configuring and integrating VertexAI components into client environments, particularly in scenarios involving complex network architectures and stringent security limitations. This individual will play a pivotal role in delivering robust, scalable, and secure AI solutions, ensuring that our clients can leverage the full potential of GCP and VertexAI in even the most challenging infrastructure settings.
Key Responsibilities:
- VertexAI Integration & Architecture: Design, implement, and maintain VertexAI components (such as Vertex Pipelines, AutoML, Model Monitoring, and Feature Store) within the client’s cloud environment, addressing any network limitations or compliance constraints.
- GCP Cloud Infrastructure Design & Deployment: Build, manage, and optimize scalable, secure, and cost-efficient GCP infrastructure to support AI/ML workloads, ensuring high availability and resilience across projects.
- Network Optimization: Identify, troubleshoot, and resolve network bottlenecks and restrictions, ensuring seamless integration and communication between VertexAI services and other infrastructure components.
- Project Leadership: Lead and oversee the end-to-end lifecycle of AI/ML projects on GCP and VertexAI, from initial architecture design to deployment, monitoring, and scaling in production environments.
- Collaboration with Cross-Functional Teams: Work closely with data scientists, machine learning engineers, and client teams to ensure the successful deployment of AI models and solutions.
- Security & Compliance: Implement and enforce security best practices, including the use of IAM policies, VPC Service Controls, and encryption to ensure that VertexAI components meet regulatory requirements and data privacy standards.
- Performance Optimization: Continuously assess and enhance system performance, optimizing the infrastructure and AI model deployment for low latency, high throughput, and efficient resource utilization.
- Automation & CI/CD Pipelines: Design and implement automation scripts and CI/CD pipelines for the efficient and error-free deployment of VertexAI models, pipelines, and data workflows.
- Client Consultation: Provide expert guidance and advisory services to clients on best practices for VertexAI and GCP cloud solutions, ensuring that their business needs are met with scalable and innovative AI technologies.
Requirements:
- Expertise in VertexAI: In-depth knowledge of VertexAI components and experience with deploying AI/ML models in real-world production environments using VertexAI services such as Vertex Pipelines, Model Monitoring, Feature Store, AutoML, and Vertex Notebooks.
- Google Cloud Platform Proficiency: Deep experience with GCP services such as Google Kubernetes Engine (GKE), Cloud Functions, BigQuery, Pub/Sub, and Cloud Storage, and their integration with VertexAI.
- Networking Expertise: Proven ability to configure, manage, and troubleshoot complex network environments, including VPC Peering, VPN, private Google Access, firewall rules, and VPC Service Controls, especially in restricted or highly regulated environments.
- Proven Track Record: Demonstrated experience with 3+ projects utilizing GCP and VertexAI, preferably in environments with significant network restrictions or security considerations.
- Cloud Security & Compliance: Strong understanding of cloud security principles, including IAM roles, KMS, data encryption, and identity-aware proxy (IAP) for securing applications in the cloud.
- Automation & Infrastructure as Code (IaC): Hands-on experience with automation tools and technologies like Terraform, Cloud Deployment Manager, or Ansible to manage cloud infrastructure.
- AI/ML Experience: Familiarity with machine learning model development, MLOps best practices, and experience collaborating with data science teams on AI-driven projects.
- Problem-Solving Skills: Strong analytical and troubleshooting skills, with a focus on resolving complex technical issues that involve both AI model deployment and cloud infrastructure challenges.
- Communication Skills: Excellent verbal and written communication skills, with the ability to clearly articulate complex technical solutions to both technical and non-technical stakeholders.
Preferred Qualifications:
- GCP Certification: Google Professional Cloud Architect, Google Professional Data Engineer, or equivalent certifications.
- AI/ML Certifications: Certifications in AI/ML frameworks or technologies.
- Experience in hybrid cloud or multi-cloud environments.
Hands-on experience with Kubernetes for containerized AI/ML workloads
