Google Cloud Platform AI Engineer /Technical Lead

Google Cloud Platform AI Engineer /Technical Lead

Posted Today by 1763792426

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Negotiable
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Remote
USA
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Artificial Intelligence BigQuery Cloud Computing Cloud Storage Cloud Technology Containerisation Continuous Integration and Continuous Delivery Data Engineering Dataflow Data Ingestion Data Science Feature Engineering Google Cloud Google Cloud Platform (GCP) Machine Learning Microservices Python (Programming Language) PyTorch (Machine Learning Library) Scalability Scikit-learn (Machine Learning Library) Stakeholder Management Systems Architecture Verification And Validation

Summary: The Google Cloud Platform AI Engineer / Hands-on Technical Lead is responsible for designing, building, and delivering scalable AI/ML solutions on Google Cloud Platform. This role requires a blend of strategic oversight and hands-on implementation, ensuring that architectural visions are realized through robust production-grade solutions. The engineer will lead the development of machine learning models and MLOps frameworks while facilitating collaboration between various technical teams. The position emphasizes adherence to architectural standards and best practices in AI integration across the organization.

Key Responsibilities:

  • Collaborate with enterprise and solution architects to define AI/ML system architecture, reference patterns, and integration models on Google Cloud Platform.
  • Translate architectural designs into hands-on implementation using Vertex AI, BigQuery, Dataflow, Pub/Sub, Cloud Run, and related Google Cloud Platform services.
  • Lead end-to-end development of machine learning models, including data ingestion, feature engineering, training, validation, deployment, and monitoring.
  • Build and maintain MLOps pipelines aligned with architectural standards (CI/CD, automated deployments, model governance).
  • Provide technical expertise in choosing appropriate Google Cloud Platform services, ML frameworks, and design patterns.
  • Champion cloud-native best practices in security, scalability, reliability, and cost optimization.
  • Mentor and guide engineering and data science team members, ensuring consistent alignment with architectural direction.
  • Conduct design reviews, code reviews, and contribute to architecture documentation and decision records.

Key Skills:

  • Strong hands-on expertise with Google Cloud Platform AI/ML ecosystem (Vertex AI, BigQuery, Dataflow, Cloud Storage, Pub/Sub, Cloud Run).
  • Proficiency in Python, ML frameworks (TensorFlow, PyTorch, Scikit-learn), and containerization (Docker, Kubernetes/GKE).
  • Good understanding of cloud architecture, microservices, event-driven patterns, and data engineering pipelines.
  • Experience building scalable, secure MLOps frameworks and CI/CD pipelines.
  • Excellent communication and collaboration skills for working with architects, stakeholders, and cross-functional teams.

Salary: undetermined

City: undetermined

Country: USA

Working Arrangements: remote

IR35 Status: outside IR35

Seniority Level: undetermined

Industry: IT

Detailed Description From Employer:

Job Title: Google Cloud Platform AI Engineer / Hands-on Technical Lead (Client Architect Collaboration necessary)
Location: Hartford, CT/Remote working in EST Hours

The Google Cloud Platform AI Engineer / Hands-on Technical Lead partners closely with enterprise and solution architects to design, build, and deliver scalable AI/ML solutions on Google Cloud Platform. This role is both strategic and deeply hands-on, ensuring that architectural vision is translated into robust, production-grade implementations. The engineer leads the development of ML models, data pipelines, and MLOps frameworks while aligning designs with architectural standards, security guidelines, and best practices. The position also acts as a technical bridge between architecture teams, data science teams, and application engineering groups to ensure seamless integration of AI capabilities across the organization.


Key Responsibilities

  • Collaborate with enterprise and solution architects to define AI/ML system architecture, reference patterns, and integration models on Google Cloud Platform.
  • Translate architectural designs into hands-on implementation using Vertex AI, BigQuery, Dataflow, Pub/Sub, Cloud Run, and related Google Cloud Platform services.
  • Lead end-to-end development of machine learning models, including data ingestion, feature engineering, training, validation, deployment, and monitoring.
  • Build and maintain MLOps pipelines aligned with architectural standards (CI/CD, automated deployments, model governance).
  • Provide technical expertise in choosing appropriate Google Cloud Platform services, ML frameworks, and design patterns.
  • Champion cloud-native best practices in security, scalability, reliability, and cost optimization.
  • Mentor and guide engineering and data science team members, ensuring consistent alignment with architectural direction.
  • Conduct design reviews, code reviews, and contribute to architecture documentation and decision records.

Key Skills

  • Strong hands-on expertise with Google Cloud Platform AI/ML ecosystem (Vertex AI, BigQuery, Dataflow, Cloud Storage, Pub/Sub, Cloud Run).
  • Proficiency in Python, ML frameworks (TensorFlow, PyTorch, Scikit-learn), and containerization (Docker, Kubernetes/GKE).
  • Good understanding of cloud architecture, microservices, event-driven patterns, and data engineering pipelines.
  • Experience building scalable, secure MLOps frameworks and CI/CD pipelines.
  • Excellent communication and collaboration skills for working with architects, stakeholders, and cross-functional teams.
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