Job Title

We are seeking a highly skilled AI/ML Architect to design, develop, and deploy machine learning models and AI-driven solutions that enhance business processes and product capabilities. The ideal candidate will have strong expertise in data science, algorithm development, and software engineering, with a passion for solving complex problems using cutting-edge technologies.Key Responsibilities:Model Development: Build, train, and optimize machine learning models for predictive analytics, natural language processing, computer vision, and other AI applications.Data Engineering: Preprocess, clean, and structure large datasets for model training and evaluation.Deployment & Integration: Implement ML models into production systems using scalable frameworks and APIs.Research & Innovation: Stay updated with the latest AI/ML trends and incorporate best practices into solutions.Performance Monitoring: Continuously monitor and improve model accuracy, efficiency, and robustness.Collaboration: Work closely with cross-functional teams including software engineers, product managers, and data analysts to deliver AI-driven features.Required Skills & Qualifications:Bachelor’s or Master’s degree in Computer Science, Data Science, or related field.10 years of relevant experienceStrong proficiency in Python, TensorFlow, PyTorch, and ML libraries.Extensive experience designing and implementing AI/ML architectures in enterprise environments.Experience with data pipelines, feature engineering, and model deployment.Define and evolve the technical architecture for the modernization.Lead end-to-end solution design, ensuring scalability, performance, and security.Establish architectural standards, patterns, and best practices.Hands-on experience with AI/ML integration and intelligent agent frameworks.Knowledge of cloud platforms (Azure, AWS, GCP) and containerization (Docker, Kubernetes).Excellent problem-solving and analytical skills.Preferred Skills:Experience with deep learning, transformer models, and generative AI.Understanding of big data technologies (Spark, Hadoop).Exposure to AI ethics, bias mitigation, and responsible AI practices.