AI Architect

We are looking for a highly skilled Senior AI Engineer / AI Architect to lead the design, development, and deployment of scalable AI solutions.

In this role, you will combine hands-on model development with high-level system architecture to help shape our AI strategy and deliver production-ready intelligent systems.

You will work closely with engineering, product, and leadership teams to transform business needs into powerful AI-driven solutions.

Key Responsibilities

AI Architecture & System Design

• Design end-to-end AI/ML system architecture from data ingestion to deployment and monitoring.
• Define scalable and reliable ML pipelines.
• Select the appropriate tools, frameworks, and infrastructure.
• Ensure performance, security, scalability, and maintainability of AI systems.
• Design APIs and AI services for integration with products and platforms.
  
  

Model Development

• Develop, train, and fine-tune machine learning and deep learning models.
• Work on advanced AI solutions such as LLMs, NLP systems, or computer vision models.
• Optimize models for accuracy, speed, and cost efficiency.
• Conduct experiments and evaluate model performance using best practices.
  
  

MLOps & Deployment

• Build and maintain CI/CD pipelines for machine learning workflows.
• Deploy models to production using containers and cloud services.
• Implement monitoring, logging, and automated retraining processes.
• Manage model lifecycle, versioning, and performance tracking.
  
  

Technical Leadership

• Provide technical leadership to AI engineers and data scientists.
• Review code and guide best practices in AI development.
• Collaborate with cross-functional teams to align AI solutions with business goals.
• Contribute to technical strategy and AI roadmap planning.
  
  

Required Qualifications

• 7+ years of experience in AI / Machine Learning engineering.
• Strong programming skills in Python.
• Hands-on experience with PyTorch or TensorFlow.
• Experience working with LLMs and modern AI frameworks.
• Strong understanding of system design and scalable architectures.
• Experience with cloud platforms such as AWS, GCP, or Azure.
• Experience with Docker and Kubernetes.
• Familiarity with MLOps tools such as MLflow, Airflow, or similar.
• Solid understanding of data pipelines and data engineering concepts.