Artifical Intelligence & Machine Learning Engineer

We are seeking a highly skilled and innovative Artificial Intelligence and Machine Learning Engineer to join our team. The ideal candidate will be responsible for designing, developing, and deploying AI and ML models that drive impactful solutions across various domains, including education, data analytics, automation, and smart systems. You will work alongside interdisciplinary teams to integrate cutting-edge technologies into practical applications, transforming data into intelligent action. Key Responsibilities: Model Development & Deployment Design, train, evaluate, and deploy machine learning and deep learning models. Build and maintain scalable AI solutions for real-time or batch processing systems. Optimize model performance, including accuracy, latency, and resource consumption.Data Engineering & Processing Collaborate with data engineers and analysts to source, clean, and transform data. Apply advanced statistical and data mining techniques to extract meaningful patterns.Research & Innovation Stay updated with the latest advancements in AI and ML technologies. Prototype innovative solutions, contribute to research publications, and recommend adoption of new frameworks.Systems Integration Develop APIs and services to integrate AI solutions into existing platforms or applications. Collaborate with software engineers and Dev Ops teams to ensure production-level stability.Collaboration & Communication Work closely with academic and operational teams to understand business needs and translate them into technical solutions. Prepare documentation, reports, and presentation materials to communicate findings and methodologies.Requirements Required Qualifications: Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related field (Master’s or Ph D preferred). Minimum 3 years of hands-on experience in AI/ML engineering or data science roles. Proficient in Python and common ML libraries (e.g., Tensor Flow, Py Torch, scikit-learn, Keras). Strong understanding of machine learning algorithms, neural networks, natural language processing (NLP), and computer vision. Experience in model deployment using cloud platforms (e.g., AWS, Azure, GCP) or containerized environments (Docker, Kubernetes). Desirable Skills: Knowledge of MLOps tools and CI/CD pipelines for ML projects. Familiarity with big data platforms (e.g., Spark, Hadoop). Experience with academic or educational systems (LMS, AI in education) is a plus. Demonstrated contributions to open-source projects, research publications, or hackathons.#J-18808-Ljbffr