Are you looking to kick-start your career in technology with a structured development path and fast-track growth opportunities? Our IBM Tech Sales Accelerator Program is designed for ambitious graduates, offering hands-on training, mentorship, and real-world experience within IBM Client Engineering. As an AI Engineer within our Client Engineering team, you'll harness your unique skills and perspectives to engage in the development and deployment of AI systems using our watsonx platform, creating 4-to-8-week pilots for clients, and contributing to IBM's story of growth and innovation.
Over 12-24 months, you will work in multi-disciplinary pre-sales team, gaining critical skills before transitioning into your next career step as Partner/Brand Technical Specialist.
As Client Engineering, developing trusted relationships, you’ll work with innovative partners to shape the future of their technology landscape, creating (4-6 week) pilots for clients, and contributing to IBM’s story of growth and technology innovation. As we help you combine your technical education with consultative sales best practices, you will accelerate enterprises’ success by providing award-winning solutions across Generative AI, Machine Learning, Operations Research, Data Governance and many more.
Your Responsibilities Include
- Proof of Concept (POC) Development: Develop POCs to validate and highlight the feasibility and effectiveness of the proposed AI solutions. Collaborate with development teams to implement and iterate on POCs, ensuring alignment with customer requirements and expectations.
- Collaboration: Collaborate with cross-functional teams to ensure smooth execution and successful delivery of AI solutions. Effectively communicate progress, risks, and dependencies to stakeholders.
- Solution Implementation and Deployment: Oversee the implementation and deployment of AI Pilots, working closely with development teams to ensure adherence to best practices, quality standards, and performance requirements. Provide technical guidance and support during the implementation phase.
- Customer Engagement and Support: Provide technical support during the solution development phase and offer guidance on AI-related best practices and use cases.
- Documentation and Knowledge Sharing: Document solution architectures, design decisions, implementation details, and lessons learned. Contribute to internal knowledge sharing initiatives and mentor new team members.
Industry Trends and Innovation: Stay up to date with the latest trends and advancements in AI, foundation models, and large language models. Evaluate emerging technologies, tools, and frameworks to assess their potential impact on solution design and implementation.
- AI-Related Education: Possess a Bachelor's, Master's, or Ph.D. degree in Computer Science, Artificial Intelligence, Mathematics, Physics, or a related field.
- Good understanding of foundation models, large language models, machine learning, deep learning algorithms and the application of technology for the governance of data and models.
- Strong programming skills: Proficiency in Python and experience with AI frameworks such as TensorFlow, PyTorch, Keras or Hugging Face. Understanding in the usage of libraries such as SciKit Learn, Pandas, Matplotlib, etc. Familiarity with cloud platforms (e.g. IBM, Kubernetes, AWS, Azure, GCP) and related services is a plus.
- Excellent interpersonal and communication skills: Engage with stakeholders for analysis and implementation. Commitment to continuous learning and staying updated with advancements in the field of AI.
- Growth mindset: Demonstrate a growth mindset to understand clients' business processes and challenges.
- Team player with a collaborative spirit, able to work across multiple functions and levels within IBM and partners.
- First experience with IT and/or Sales.
- IBM is seeking a dynamic, enthusiastic, and results-oriented individual with a profound passion for AI and Data Science.
- The ideal candidate is also able to leverage state-of-the-art AI frameworks and engineering practices to design, build, and deploy scalable machine learning and foundation model solutions across hybrid cloud environments. Familiarity with MLOps pipelines, model serving and monitoring, data versioning, and responsible AI principles is a plus. Any hands-on experience with AI/ML services from major cloud providers is greatly appreciated.