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.
