SAP Technical Consultant; Advanced Job Menlyn Gauteng South Africa,IT/Tech

Position: 2496 SAP Technical Consultant (Advanced)
Location: Menlyn

ESSENTIAL

SKILLS:

Kindly note that this role is for an AI Engineer in a SAP team.
1. Python Programming

Strong understanding of Python syntax and principles of clean, reusable code.
Understanding of Python frameworks and libraries e.g. Pandas and Num Py

2. AWS Lambda Functions

Experience developing, deploying, and managing serverless applications using AWS Lambda
Proficiency in writing unit and integration tests for AWS Lambda functions to ensure functionality, performance, and reliability.

3. Version Control with Git

Experience with Git repositories and code management (e.g. Git Hub)
Proficiency in using Git for version control and collaboration.

4. Testing Frameworks

Experience with testing tools like pytest for creating and maintaining unit tests.
Ability to implement automated testing pipelines to ensure code reliability.

5. API Management

API designing principles and best practices.
Experience in working with RESTful APIs and understanding how to consume and create APIs.
Familiarity with API management tools, such as Apigee, for efficient and secure API integration.

6. Cloud Platforms

Understanding how to deploy application applications in AWS.

7. IDE And Tools

Visual studio code
Git Hub Desktop
Py Charm
Jupyter Notebook
Any additional responsibilities assigned in the Agile Working Model (AWM) Charter

ADVANTAGEOUS SKILLS:

Strong understanding of SQL and experience writing complex queries for data extraction and manipulation.
Basic understanding of Large Language Models (LLMs) like GPT-4 and their applications.
Familiarity with integrating LLM APIs (e.g., OpenAI, Hugging Face) into Python applications is a plus.
Experience and Knowledge about SAP Analytics Stack and Tools.
Experience in designing and creating data models within SAP Datasphere leveraging Python for data transformation tasks.
Experience in developing and implementing data integration solutions to connect various data sources to SAP Datasphere using Python scripts and APIs.
Implementing and managing infrastructure on AWS using Terraform.
Understanding and experience with Generative AI use cases or projects
Coaching and giving training to fellow colleagues and users when required.
Problem solving capabilities.
Strong presentation skills

RESPONSIBILITIES:

The AI Engineer is a versatile and highly skilled professional responsible for leveraging advanced data analytics, machine learning, and artificial intelligence techniques to drive business insights, optimize operations, and deliver innovative solutions.
This role encompasses a wide range of responsibilities, from creating data mining architectures and statistical models to implementing AI-powered prompt engineering strategies.

1. Data Analytics and Modelling:

Create data mining architectures, models, and protocols to identify trends and patterns in large data sets across various business functions, including market economics, supply chain, marketing, and scientific research.
Apply advanced statistical and data analysis methodologies to derive actionable insights that inform business decisions.
Research and stay up to date with emerging data science principles, theories, and techniques to continuously enhance the organization's analytical capabilities.

2. Machine Learning and AI Integration:

Develop and implement machine learning (ML) or other artificial intelligence (AI) techniques to solve business problems and derive actionable insights.
Architect and implement prompt engineering strategies, leveraging advanced Natural Language Processing (NLP) techniques, to guide and control the behavior of AI language models.
Collaborate with cross-functional teams to refine the prompt generation process and align the AI system's output with broader organizational goals and user needs.

3. AI-Powered Content Generation:

Craft, refine, and optimize the generation of AI-generated text prompts to ensure they are contextually accurate, engaging, and relevant for a wide array of applications, from conversational agents to automated content creation.
Apply machine learning methodologies, including training data curation, feature engineering, model evaluation, and hyperparameter tuning, to continually improve the performance of AI systems.
Knowledge and experience with use of Large Language Models (LLM) and Retrieval Augmented Generation (RAG) pipelines

4. AI System Integration and Deployment:

Integrate AI systems and models into existing applications and processes, ensuring seamless integration and optimal performance.
Develop strategies and frameworks for deploying AI solutions at scale, addressing challenges related to scalability, reliability, and security.
Collaborate with software engineers and IT teams to ensure the successful implementation and maintenance of AI-powered systems.

5. Stakeholder Engagement and Collaboration:

Provide guidance and support to help the organization leverage data-driven decision-making and AI-enabled capabilities.
Collaborate with subject matter experts and domain specialists to understand business requirements and translate them…