AI Technology and Innovation Engineer Job Johannesburg area,South Africa,Software Development

JOB DESCRIPTION

Make an impact with NTT DATA
Join a company that is pushing the boundaries of what is possible. We are renowned for our technical excellence and leading innovations, and for making a difference to our clients and society. Our workplace embraces diversity and inclusion – it’s a place where you can grow, belong and thrive.

Your day at NTT DATA

The AI Solutions Engineer is an advanced subject matter expert, responsible for actively participating in identifying high-value use cases, assessing solution feasibility, prototyping innovative ideas, and delivering successful pilot projects.

This role demands specialist technical knowledge, proficiency in software engineering, and capability in data engineering and cloud infrastructure.

The AI Solutions Engineer is expected to ensure secure, reliable, and scalable integrations with existing enterprise platforms, systems, and data sources.

Key Responsibilities:

Develop, fine-tune, and deploy AI models, including large language models (LLMs) such as GPT-4 or open-source equivalents.

Design and implement effective prompt engineering strategies and optimizations to enhance AI accuracy, consistency, and reliability.

Engage with internal stakeholders and clients to understand business needs, translating them into actionable AI solutions.

Rapidly prototype, test, and iterate AI applications using advanced Python programming and relevant frameworks.

Integrate AI solutions securely with existing enterprise systems (CRM, ERP, HRIS, finance platforms, collaboration software) via API development and integration.

Build, maintain, and optimize end-to-end data pipelines to ensure accurate and timely data delivery for AI models.

Manage structured and unstructured datasets, leveraging vector databases and semantic search to enhance knowledge management capabilities.

Deploy, manage, and scale AI solutions within cloud computing environments (Azure, AWS, GCP), ensuring high availability, performance, and cost efficiency.

Implement Dev Ops and MLOps practices, including automated deployment, testing, monitoring, and version control, to efficiently manage the AI model lifecycle.

Ensure AI solutions adhere to industry standards and compliance regulations (GDPR, HIPAA), emphasizing security and privacy best practices.

Identify and mitigate risks associated with AI deployments, proactively addressing ethical considerations, biases, and unintended consequences.

Collaborate closely with business and functional teams to streamline processes through intelligent automation and deliver measurable business outcomes.

Provide clear documentation of technical designs, project plans, and operational procedures.

Contribute to the continuous improvement of AI best practices, methodologies, and internal frameworks.

Stay abreast of the latest AI and machine learning developments, continuously evaluating emerging technologies and methodologies.

To thrive in this role, you need to have:

Advanced understanding of artificial intelligence, natural language processing (NLP), and machine learning principles.

Advanced expertise in selecting, fine-tuning, and deploying large and small language models (LLMs/SLMs), such as OpenAI’s GPT series and open-source alternatives.

Advanced proven experience with prompt engineering, prompt optimization, and AI model reliability and accuracy improvements.

Advanced proficiency in Python programming, essential for rapid prototyping, integration, and model implementation. Python is the preferred language for AI; strong proficiency in Python is essential due to the extensive use of frameworks, libraries, and models.

Advanced knowledge of additional programming languages (optional, but valuable):

JavaScript / Type Script:
Helpful if building frontend interfaces or web integrations.

Java / C#:
Beneficial for integrations with enterprise backend systems (e.g., ERP, CRM).

Advanced familiarity with full-stack software development, including frontend and backend integration, user experience considerations, and system interoperability.

Robust knowledge of data pipeline development, data engineering concepts, and handling of structured and unstructured data.

Advanced proficiency in cloud…