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 ResponsibilitiesAI Architecture & System DesignDesign 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 DevelopmentDevelop, 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 & DeploymentBuild 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 LeadershipProvide 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 Qualifications7+ 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.