Job description

Description: Local candidates preferred. Job Summary We are seeking an experienced GCP AI/ML & Predictive Analytics Subject Matter Expert (SME) to design, develop, deploy, and support enterprise AI/ML and predictive analytics solutions on Google Cloud Platform (GCP). This role will provide technical leadership for AI and data science initiatives, partnering with business, engineering, architecture, and data teams to deliver scalable, secure, and production-ready solutions. The successful candidate will possess strong experience in machine learning, predictive analytics, cloud-based AI/ML platforms, data engineering, and MLOps practices, with demonstrated ability to translate business requirements into actionable AI-driven solutions. Key Responsibilities AI/ML Solution Architecture and Delivery Design, develop, and implement scalable AI/ML and predictive analytics solutions using Google Cloud Platform (GCP). Lead architecture discussions and provide technical guidance for AI/ML initiatives. Develop, deploy, monitor, and optimize machine learning models and AI services. Design and implement end-to-end machine learning pipelines and MLOps processes. Develop predictive analytics solutions to support business decision-making and operational improvements. Evaluate and recommend AI/ML technologies, tools, and architectural approaches. Platform Engineering and MLOps Build and maintain AI/ML solutions using GCP services, including: Vertex AI BigQuery Cloud Storage Dataflow Dataproc Pub/Sub Cloud Functions Establish machine learning operational standards, governance processes, and lifecycle management practices. Implement model monitoring, performance tracking, model validation, and retraining processes. Ensure scalability, reliability, performance, and maintainability of AI/ML platforms. Predictive Analytics and Data Science Design and implement predictive analytics solutions, including: Forecasting Recommendation systems Classification models Regression models Anomaly detection Perform data exploration, feature engineering, model training, evaluation, and optimization. Identify opportunities to leverage AI and predictive analytics to solve business challenges and improve outcomes. Support model performance assessment using appropriate statistical and machine learning techniques. Collaboration and Leadership Partner with business stakeholders to understand requirements and identify AI/ML opportunities. Collaborate with data engineers, software engineers, architects, and product teams to deliver integrated solutions. Communicate technical concepts, trade-offs, and recommendations to technical and non-technical audiences. Mentor team members on GCP, AI/ML technologies, predictive analytics practices, and MLOps methodologies. Contribute to architectural standards, best practices, and reusable solution patterns. Required Qualifications Bachelor's degree in Computer Science, Data Science, Engineering, Information Systems, Mathematics, Statistics, or a related field; or an equivalent combination of education, training, military experience, and relevant work experience. Minimum of 10 years of experience in Data Engineering, Data Science, Machine Learning, Artificial Intelligence, Analytics, or related technical disciplines. Experience designing, developing, deploying, and supporting enterprise AI/ML solutions. Experience with Google Cloud Platform (GCP) services, including one or more of the following: Vertex AI BigQuery Cloud Storage Dataflow Dataproc Pub/Sub Cloud Functions Experience developing, deploying, monitoring, and maintaining machine learning models in production environments. Experience building end-to-end AI/ML pipelines and implementing MLOps practices. Experience with predictive analytics methodologies, including forecasting, recommendation systems, classification, regression, and anomaly detection. Strong programming experience in Python. Experience with one or more machine learning frameworks such as: TensorFlow Scikit-learn PyTorch Equivalent machine learning frameworks Knowledge of: Data preparation and transformation Feature engineering Model validation and testing Model performance optimization Machine learning lifecycle management Proven ability to collaborate effectively with cross-functional business and technical stakeholders. Strong verbal, written, and presentation communication skills. Ability to work from or relocate to one of the following approved locations: San Francisco Bay Area, CA; Charlotte, NC; Dallas, TX; Phoenix, AZ; or New York, NY. Preferred Qualifications Experience with Generative AI, Large Language Models (LLMs), AI agents, or AI orchestration frameworks. Experience with data visualization and analytics tools such as: Power BI Looker Tableau Similar reporting and visualization platforms Experience with: Kubernetes Docker CI/CD pipelines Infrastructure automation Google Cloud certifications such as: Professional Machine Learning Engineer Professional Data Engineer Related cloud or AI certifications Experience implementing AI governance, model risk management, or responsible AI practices. Experience supporting enterprise-scale AI/ML solutions in regulated environments.

Requirements

Must-haves

AI/ML Solution Architecture and Delivery
Design, develop, and implement scalable AI/ML and predictive analytics solutions using Google Cloud Platform (GCP).
Lead architecture discussions and provide technical guidance for AI/ML initiatives.
Develop, deploy, monitor, and optimize machine learning models and AI services.
Design and implement end-to-end machine learning pipelines and MLOps processes.
Develop predictive analytics solutions to support business decision-making and operational improvements.
Evaluate and recommend AI/ML technologies, tools, and architectural approaches.
Platform Engineering and MLOps
Build and maintain AI/ML solutions using GCP services, including:
Cloud Storage
Cloud Functions
Establish machine learning operational standards, governance processes, and lifecycle management practices.
Implement model monitoring, performance tracking, model validation, and retraining processes.
Ensure scalability, reliability, performance, and maintainability of AI/ML platforms.
Predictive Analytics and Data Science
Design and implement predictive analytics solutions, including:
Recommendation systems
Classification models
Regression models
Anomaly detection

Nice-to-haves

Local candidates preferred.
Experience with Generative AI, Large Language Models (LLMs), AI agents, or AI orchestration frameworks.
Experience with data visualization and analytics tools such as:
Similar reporting and visualization platforms

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

gcpaimlmachine learningpythonkubernetesdockerci/cd

Platform: jobdiva
Effort: medium