Principal AI/ML Solutions Engineer

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 experience • Strong 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.