AI Solutions Architect
GSK
4 months ago
Remote friendly (Durham, NC)
United States
IT
Key Responsibilities:
- Architect and lead the development of an enterprise-grade AIOps platform leveraging machine learning, deep learning, and advanced analytics.
- Design scalable AI pipelines for anomaly detection, predictive analytics, root cause analysis, and intelligent alerting.
- Collaborate cross-functionally with engineering, DevOps, and IT teams to integrate AI capabilities into existing operational workflows and tools.
- Evaluate and select appropriate technologies, frameworks, and cloud-native services in Azure and GCP to support real-time data ingestion, processing, and model deployment.
- Ensure platform reliability and performance, with a focus on scalability, security, and maintainability.
- Mentor and guide engineering teams on best practices in AI architecture and model lifecycle management.
- Stay current with emerging trends in AIOps, MLOps, and IT automation to continuously evolve the platform.
Basic Qualifications (Required):
- Bachelor’s or master’s degree in computer science, Data Science, Machine Learning, or related field.
- Experience as a Solution Architect or AI/ML Architect in enterprise environments.
- 3+ years of hands-on experience building AIOps platforms and solutions in IT environments.
- Experience in IT Operations, infrastructure monitoring, incident management, and observability tools.
- Experience with AI/ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn), data engineering tools (e.g., Spark, Kafka, Airflow), and knowledge graphs.
- Experience in Python and familiarity with key AI/ML libraries (e.g., TensorFlow, PyTorch, HuggingFace).
- Experience with Azure and GCP, including native Data/AI-ML toolsets and services (e.g., ADLS, Azure Machine Learning, Azure Foundry, GCS, GCP Vertex AI), container orchestration (Kubernetes), and hands-on work with Large Language Models (LLMs) and Generative AI tools.
Preferred Qualifications:
- Experience with log and metrics analysis, time-series forecasting, and NLP for IT ticket classification.
- Knowledge of MLOps practices for model deployment, monitoring, and governance.
- Exposure to tools like ServiceNow, Datadog, Prometheus, Grafana, ELK Stack, etc.
- Certified in AI engineering in Azure and GCP.
Application Instructions (included for accommodations): If you require an accommodation or other assistance to apply, contact Recruitment Staff at usrecruitment.adjustments@gsk.com.
- Architect and lead the development of an enterprise-grade AIOps platform leveraging machine learning, deep learning, and advanced analytics.
- Design scalable AI pipelines for anomaly detection, predictive analytics, root cause analysis, and intelligent alerting.
- Collaborate cross-functionally with engineering, DevOps, and IT teams to integrate AI capabilities into existing operational workflows and tools.
- Evaluate and select appropriate technologies, frameworks, and cloud-native services in Azure and GCP to support real-time data ingestion, processing, and model deployment.
- Ensure platform reliability and performance, with a focus on scalability, security, and maintainability.
- Mentor and guide engineering teams on best practices in AI architecture and model lifecycle management.
- Stay current with emerging trends in AIOps, MLOps, and IT automation to continuously evolve the platform.
Basic Qualifications (Required):
- Bachelor’s or master’s degree in computer science, Data Science, Machine Learning, or related field.
- Experience as a Solution Architect or AI/ML Architect in enterprise environments.
- 3+ years of hands-on experience building AIOps platforms and solutions in IT environments.
- Experience in IT Operations, infrastructure monitoring, incident management, and observability tools.
- Experience with AI/ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn), data engineering tools (e.g., Spark, Kafka, Airflow), and knowledge graphs.
- Experience in Python and familiarity with key AI/ML libraries (e.g., TensorFlow, PyTorch, HuggingFace).
- Experience with Azure and GCP, including native Data/AI-ML toolsets and services (e.g., ADLS, Azure Machine Learning, Azure Foundry, GCS, GCP Vertex AI), container orchestration (Kubernetes), and hands-on work with Large Language Models (LLMs) and Generative AI tools.
Preferred Qualifications:
- Experience with log and metrics analysis, time-series forecasting, and NLP for IT ticket classification.
- Knowledge of MLOps practices for model deployment, monitoring, and governance.
- Exposure to tools like ServiceNow, Datadog, Prometheus, Grafana, ELK Stack, etc.
- Certified in AI engineering in Azure and GCP.
Application Instructions (included for accommodations): If you require an accommodation or other assistance to apply, contact Recruitment Staff at usrecruitment.adjustments@gsk.com.