Technical Consultant-Cloud Platform Engineering Services chez IBM

Introduction

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You’ll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you’ll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You’ll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.

Your role and responsibilities

As a Technical Consultant in Cloud Platform Engineering Services:

Your primary responsibilities will include:

• Compute Fundamentals: Demonstrating a deep understanding of compute concepts, including virtualization, containerization, operating systems, and system administration.

• Cloud Technologies: Demonstrate experience with cloud platforms like GCP, AWS, and Azure, including their AI/ML services.

• Demonstrate Generative AI Experience with LLM and Generative AI and Google Cloud Products and services (e.g Vertex AI, Dialogflow, Gemini)

• ML Development: Demonstrate experience with Machine Learning model development and deployment.

• AIML Frameworks: Demonstrate experience with frameworks for deep learning (e.g.PyTorch, Tensorflow, Jax, Ray, etc.), AI accelerators (e.g. TPUs, GPUs), model architectures (e.g. encoders, decoders, transformers), and using machine learning APIs.

Required technical and professional expertise

• Exposure to Cloud Platforms: Familiarity with cloud platform engineering services, including architecture, deployment, and management of cloud-based systems.

• Experience with Skill Taxonomies: Knowledge of skill taxonomy development and implementation, including categorization, mapping, and maintenance of technical skills.

• Integration Expertise: Exposure to integrating new offerings into existing practices, ensuring minimal disruption and optimal results.

• Cloud Service Offerings: Familiarity with cloud service offerings, including infrastructure, platform, and software as a service (IaaS, PaaS, SaaS) models.

• Compute Fundamentals: Demonstrating a deep understanding of compute concepts, including virtualization, containerization, operating systems, and system administration.

• Cloud Technologies: Demonstrate experience with cloud platforms like GCP, AWS, and Azure, including their AI/ML services.

• Demonstrate Generative AI Experience with LLM and Generative AI and Google Cloud Products and services (e.g Vertex AI, Dialogflow, Gemini)

• ML Development: Demonstrate experience with Machine Learning model development and deployment.

• AIML Frameworks: Demonstrate experience with frameworks for deep learning (e.g.PyTorch, Tensorflow, Jax, Ray, etc.), AI accelerators (e.g. TPUs, GPUs), model architectures (e.g. encoders, decoders, transformers), and using machine learning APIs.

Preferred technical and professional experience

• Cloud Service Models Knowledge: Familiarity with cloud service offerings, including infrastructure, platform, and software as a service (IaaS, PaaS, SaaS) models.

• Cloud Platform Architecture: Exposure to cloud platform engineering services, including architecture, deployment, and management of cloud-based systems, to support the development of a new skill taxonomy.

IBM is committed to creating a diverse environment and is proud to be an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, caste, genetics, pregnancy, disability, neurodivergence, age, veteran status, or other characteristics. IBM is also committed to compliance with all fair employment practices regarding citizenship and immigration status.