AI Engineer

SUMMARY:
Kickstart your career as an AI Engineer and help build cutting-edge AI capabilities from the ground up. This is an early-career opportunity to work across the full AI stack—data engineering, ML/AI model development, cloud infrastructure, and AI application deployment—while learning alongside a small, highly skilled team of peers.

POSITION INFO:
This role is with a leading technology company focusing on enterprise network and digital solutions. You’ll join a newly established AI transformation initiative, contributing to the development of AI infrastructure, applications, and operational workflows. The position offers a chance to shape AI strategy, deploy real-world ML/DL models, and collaborate with product managers, engineers, and data scientists.
You will gain hands-on experience with modern AI frameworks, cloud platforms, MLOps practices, and cutting-edge data engineering pipelines, all within a collaborative and technically rigorous environment. The company promotes continuous learning, innovation, and career growth in AI and machine learning domains.

Key Responsibilities:

• Design, train, and deploy ML/DL models (NLP, computer vision, time series, generative AI) and build AI applications with LLMs.
• Develop, maintain, and optimise data pipelines; gather, clean, and engineer features from diverse data sources.
• Deploy AI models to production using containerization (Docker) and orchestration (Kubernetes), ensuring operational reliability.
• Collaborate with business and technical stakeholders to translate problems into AI solutions.
• Monitor, evaluate, and maintain AI systems, including CI/CD pipelines, model versioning, and lifecycle management.
• Contribute to AI platform development, infrastructure-as-code, and workflow orchestration for complex pipelines.
• Continuously research emerging AI/ML technologies, experiment with new techniques, and share knowledge with the team.

Job Experience and Skills Required:

• Bachelor’s or Honours degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, Information Systems, or related quantitative field.
• Graduates within the last 18 months or completing studies in 2025 are encouraged to apply.

• Early-career candidates with demonstrable experience through projects, internships, competitions (Kaggle/hackathons), or portfolio work.

• Programming: Python (preferred), or similar languages (Java, JavaScript, Go).
• Database knowledge: SQL and NoSQL, vector databases exposure advantageous.
• ML fundamentals: basic understanding of machine learning concepts, algorithms, and statistics.
• Cloud platforms: AWS, Azure, or GCP.
• Tools: Git, containerization (Docker), workflow orchestration tools (Airflow, Prefect, Dagster) beneficial.
• Communication: ability to present technical concepts to business and technical audiences.

• Exposure to ML frameworks (TensorFlow, PyTorch, scikit-learn), LLMs, generative AI, MLOps platforms (MLflow, Weights & Biases).
• Familiarity with prompt engineering, feature engineering, and responsible AI practices.
• Experience with REST APIs, CI/CD pipelines, and infrastructure-as-code tools.

Apply now!