AI Architect

About NavaSys & the Role

NavaSys is building a deep AI and data engineering capability that is secure-by-design and built to scale.

As AI Architect, you will define how AI is structured, deployed and standardised across NavaSys projects. Youll combine broad architectural thinking with enough hands-on depth to validate approaches and guide teams confidently.

Key Responsibilities

• Architect and design end-to-end AI/ML solutions from data ingestion and preparation to model deployment and monitoring.
• Work with business, product, data and engineering stakeholders to identify AI opportunities and automation use cases.
• Select and evaluate appropriate AI frameworks, platforms and tools (e.g., AWS SageMaker, Azure ML, GCP Vertex AI).
• Lead and mentor data scientists, ML engineers and developers on architecture, patterns and implementation choices.
• Ensure AI models and systems are scalable, secure, explainable and compliant with organisational and regulatory standards.
• Design MLOps pipelines for continuous integration, delivery and monitoring of models.
• Conduct POCs and feasibility studies to evaluate new AI technologies and techniques and define their adoption paths.
• Partner with data engineering to define data architecture, quality, governance and accessibility standards for AI use cases.
• Develop, maintain and evangelise AI architectural standards, design patterns and documentation that teams can reuse.
• Stay current on AI trends, tools and research and translate them into actionable architectural guidance.

Preferred candidate profile

Skills Required

• Artificial Intelligence / GenAI
• AI Solutions
• MLOps
• Machine Learning
• AWS
• GenAI
• Python

Must-Have Profile

• Bachelors or Masters degree in Computer Science, Data Science, Artificial Intelligence or related field.
• Minimum 8 years of experience in AI/ML solution design and development, with at least 3 years in an architectural or leadership role.
• Strong expertise in machine learning, deep learning, NLP, computer vision and generative AI techniques.
• Proficiency in Python, R or Java and experience with ML/DL frameworks such as TensorFlow, PyTorch, Scikit-learn or Hugging Face.
• Hands-on experience with cloud platforms (AWS, Azure or GCP) and containerisation/orchestration (Docker, Kubernetes).
• Solid understanding of data engineering: ETL, data lakes, pipelines, real-time processing.
• Familiarity with MLOps tools (MLflow, Kubeflow, Airflow, CI/CD pipelines).
• Strong understanding of AI ethics, model interpretability and responsible AI principles.
• Excellent communication, documentation and leadership skills to influence cross-functional teams.

Nice-to-Have

• Certifications such as AWS Certified Solutions Architect – Professional, Databricks Certified Data Engineer or TOGAF.
• Familiarity with AI/ML frameworks (TensorFlow, PyTorch, LangChain, etc.).
• Experience in pre-sales or customer-facing architecture roles.
• Exposure to data governance, AI ethics and responsible AI practices.