Senior AI Developer

Tech Mahindra; Johannesburg; On-site; ZAR 1,000,000 - 2,000,000; Full time

Job summary

Tech Mahindra in Johannesburg is seeking a Senior AI Developer to design, build, and deploy AI models and pipelines in a greenfield environment, taking ownership of delivery.

The role covers NLP, computer vision, generative AI, MLOps, and cloud platforms like Azure ML, AWS SageMaker, Vertex AI, with expectation of leading technical delivery and mentoring junior engineers.

Qualifications

• Minimum 7 years of software development experience.
• At least 4 years focused on AI/ML engineering.
• Proven experience delivering AI/ML solutions into production.
• Experience in enterprise or regulated environments.
• Strong experience with cloud-based AI deployment.
• Experience in greenfield or newly established AI environments would be advantageous.
• Ability to work independently and take ownership of technical delivery.

Responsibilities

• Design, build, train, test, and deploy machine learning and deep learning models.
• Develop end-to-end AI pipelines covering data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment.
• Build scalable AI solutions that can operate reliably in production environments.
• Optimise models for performance, accuracy, scalability, and reliability.
• Work with structured and unstructured data to develop practical AI solutions.
• Translate business and technical requirements into AI-driven solutions.

Skills

Machine learning

Deep learning

Generative AI

MLOps

Cloud platforms

NLP

Computer vision

Production deployment

Team leadership

Education

Degree in Computer Science, Data Science, Mathematics, Engineering

Certifications in ML / Cloud (advantageous)

Tools

TensorFlow

PyTorch

Azure ML

AWS SageMaker

Vertex AI

Job description

We are looking for a highly skilled Senior AI Developer with strong hands-on experience in machine learning, deep learning, generative AI, MLOps, cloud AI platforms, NLP, computer vision, and production-grade AI deployment. This is a senior technical role suited to someone who has already built and deployed AI solutions in real-world business environments. The successful candidate will help design, build, deploy, and operate AI models and pipelines, while also contributing to the technical standards and foundations of a newly established AI function. The ideal candidate must be comfortable working in a greenfield environment, where they will be expected to work independently, solve complex technical problems, and help shape the client’s AI delivery capability.

Key Responsibilities AI Development and Engineering

• Design, build, train, test, and deploy machine learning and deep learning models.
• Develop end-to-end AI pipelines covering data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment.
• Build scalable AI solutions that can operate reliably in production environments.
• Optimise models for performance, accuracy, scalability, and reliability.
• Work with structured and unstructured data to develop practical AI solutions.
• Translate business and technical requirements into AI-driven solutions.

Generative AI and LLM Development

• Implement and fine-tune Large Language Models for enterprise use cases.
• Develop generative AI solutions using modern frameworks and tools.
• Design and build Retrieval-Augmented Generation architectures.
• Work with embeddings, vector databases, semantic search, and document intelligence.
• Evaluate LLM performance, accuracy, hallucination risk, and output quality.

MLOps and AI Infrastructure

• Establish and manage MLOps frameworks for production AI delivery.
• Implement model versioning, monitoring, retraining, performance tracking, and governance.
• Build automated AI/ML pipelines and deployment workflows.
• Implement CI/CD practices for machine learning and AI systems.
• Monitor production models for drift, degradation, and performance issues.
• Ensure AI solutions are secure, scalable, maintainable, and reliable.

Cloud AI Platform Delivery

• Deploy and manage AI workloads on cloud platforms.
• Work with platforms such as Azure AI / Azure ML, AWS SageMaker, and Google Vertex AI.
• Support batch and real-time inference workloads.
• Integrate AI models with enterprise applications and data platforms.
• Ensure cloud-based AI deployments meet performance and reliability standards.

NLP and Computer Vision

• Build NLP solutions including text processing, classification, named entity recognition, and transformer-based models.
• Develop computer vision solutions such as image classification, object detection, and segmentation.
• Deploy NLP and computer vision models into production environments where required.

Collaboration and Technical Leadership

• Work directly with business and technical stakeholders.
• Contribute to AI technical standards, best practices, and delivery frameworks.
• Produce clear technical documentation, model cards, and deployment guides.
• Support knowledge transfer within the team.
• Mentor junior team members as the AI function grows.
• Operate independently and take ownership of AI solution delivery.

Required Technical Skills Core AI / ML Skills

• Model training and optimisation
• RAG architectures
• CI/CD for ML
• Orchestration tools
• Model lifecycle management
• Production deployment practices

Frameworks Strong production-level experience required in:

• TensorFlow
• PyTorch

Cloud AI Platforms Experience with one or more of the following:

• Azure AI / Azure Machine Learning
• AWS SageMaker
• Google Vertex AI

NLP

• Text processing
• Named entity recognition
• Text classification
• Transformer-based models
• NLP model deployment

Computer Vision

• Image classification
• Object detection
• Image segmentation
• Production deployment of computer vision models

Experience Requirements The ideal candidate should have:

• Minimum 7 years’ software development experience.
• At least 4 years’ experience focused on AI / ML engineering.
• Proven experience delivering AI or ML solutions into production.
• Experience working in enterprise or regulated environments.
• Strong experience with cloud-based AI deployment.
• Experience in greenfield or newly established AI environments would be advantageous.
• Ability to work independently and take ownership of technical delivery.
• Ability to define standards and build scalable AI foundations.

Qualifications Preferred qualifications:

• Degree in Computer Science, Data Science, Mathematics, Engineering, or a related technical field. Equivalent practical experience will also be considered.
• Advantageous certifications:
• Microsoft Azure AI / Azure ML certification
• AWS Machine Learning / SageMaker certification
• Google Cloud AI / Vertex AI certification
• Data Science or Machine Learning certification