Senior AI Engineer

Key Responsibilities AI Productions Scaling: - Design, develop, and deploy scalable AI/ML systems and pipelines using cloud infrastructure (AWS, Google Cloud, Azure) - Build and maintain production-grade machine learning models for content recommendation, personalization, and newsroom automation - Implement MLOps practices including model versioning, automated testing, monitoring, and continuous deployment - Optimize AI model performance, latency, and resource utilization for high-traffic production environments Personalization s Content Intelligence: - Develop and enhance personalization algorithms that deliver relevant content experiences across web, mobile, and emerging platforms - Build content classification, semantic analysis, and recommendation systems using NLP and deep learning techniques - Implement real-time content processing pipelines for automated tagging, categorization, and content matching - Create user profiling and behavioral analysis systems to improve content targeting and engagement Newsroom AI s Editorial Tools: - Collaborate with editorial teams to develop AI-powered tools that accelerate content creation and curation - Build automated content workflows including article summarization, fact-checking assistance, and content optimization - Implement AI solutions for news aggregation, content deduplication, and trend analysis - Develop tools for automated social media content generation and distribution optimization Technical Excellence s Innovation: - Write clean, maintainable, and well-documented Python code following software engineering best practices - Design and implement APIs and microservices for AI model serving and integration - Conduct experiments with cutting-edge AI technologies including large language models (LLMs), transformer architectures, and generative AI - Stay current with AI/ML research and evaluate new technologies for potential implementation Collaborations Community Building: - Work closely with product teams to translate business requirements into technical AI solutions - Mentor junior engineers and contribute to knowledge sharing within the AI community - Participate in code reviews, technical discussions, and architectural planning sessions - Collaborate with data scientists to productionize research models and prototypes Required Skills and Experience Technical Qualifications: - Honours or Masters degree in Computer Science, Machine Learning, Data Science, or related technical field - Minimum 5+ years of software engineering experience with at least 3 years focused on AI/ML development - Strong proficiency in Python and experience with ML frameworks (TensorFlow, PyTorch, scikit-learn) - Hands-on experience with cloud platforms (AWS SageMaker, Google AI Platform, Azure ML) and containerization (Docker, Kubernetes) - Proven experience building and deploying production ML systems at scale AI/ML Expertise: - Deep understanding of machine learning algorithms, deep learning architectures, and neural networks - Strong experience with NLP, text processing, and content recommendation systems - Knowledge of MLOps practices including model monitoring, versioning, and automated retraining - Experience with vector databases, embedding-based search, and similarity matching algorithms - Familiarity with transformer models, large language models, and modern NLP architecture Software Engineering: - Proficiency in API development (REST, GraphQL) and microservices architecture - Experience with version control (Git), CI/CD pipelines, and automated testing frameworks - Strong understanding of database technologies (SQL, NoSQL) and data pipeline tools - Knowledge of software engineering best practices including design patterns and code quality standards - Preferred Experience: - Experience in media, publishing, or content-driven industries - Knowledge of content management systems and editorial workflows - Understanding of web analytics, user behavior analysis, and A/B testing methodologies - Experience with real-time streaming data processing (Kafka, Kinesis, Pub/Sub) - Familiarity with monitoring and observability tools (Prometheus, Grafana, DataDog)