Aws Ai / Ml Engineer (Remote)

Responsibilities : Machine Learning Development & Implementation (40%)- Design and implement end-to-end ML pipelines for recommendation systems, search ranking, and classification problems- Build and optimize traditional ML models using techniques such as ensemble methods, SVMs, gradient boosting, and neural networks- Develop time series forecasting models and ranking algorithms for complex business applications- Implement feature engineering pipelines that handle real-world data noise and edge cases- Create robust data preprocessing and validation systems that ensure model reliability in productionProduction ML Systems & Deployment (25%)- Deploy ML models using Docker containerization and REST API frameworks (Flask / FastAPl)- Implement model serving solutions on Azure Container Instances with proper monitoring andalerting- Build MLOps pipelines using MLflow for experiment tracking and model registry management- Design scalable data workflows using Apache Airflow and Azure Data Factory for ETL operations- Establish model versioning, rollback strategies, and performance monitoring in production environmentsTechnical Leadership & Collaboration (20%)- Serve as a technical sounding board for AI team members on ML architecture and approach decisions- Mentor team members on best practices for production ML system design and implementation- Communicate complex technical concepts clearly to both technical and non-technical stakeholders- Collaborate across AI, web development, and system architecture teams toensure seamless integration- Guide strategic decisions on when to use traditional ML versus generative AI approachesStrategic ML Decision Making (15%)- Evaluate problems to determine optimal solutions : classical ML, GenAI, or simpler analytical methods- Integrate generative AI tools effectively into workflows without over-relying on them- Design ML systems that integrate seamlessly with existing web application architectures- Provide technical guidance onmodel selection, evaluation metrics, and performance optimization- Stay current with ML best practices while maintaining focus on practical, business-driven solutionsRequired QualificationsEducation & Experience- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or related technical field- 4+ years of hands-on experience building and deploying machine learning systems in production- Proven experience working in non-technical business domains (healthcare, finance, retail, HR, etc.)- Track record of mentoring technical team members and leading collaborative projectsCore Technical Skills- Programming Excellence : Expert-level Python proficiency with focus on clean, maintainable, production-ready code- Traditional ML Expertise : Deep understanding of classification, regression, ranking, and recommendation algorithms- Production ML : Experience with MLOps practices, model deployment, monitoring, and lifecycle management- Data Engineering : Proficiency with data pipeline development, ETL processes, and handling messy real-world datasets- Cloud Platforms : Hands-on experience with Azure ML Studio, Azure Container Instances, and Azure Data FactorySpecialized Experience : - Experience building recommendation engines, search ranking systems, or time series forecasting models- Background in A / B testing methodologies and measuring business impact of ML initiatives- Knowledge of feature stores, model registry systems, and ML experiment tracking- Understanding of model interpretability, bias detection, and fairness in ML systems- Experience with both structured and unstructured data processing at scale- Experience with deep learning frameworks (TensorFlow, PyTorch) for appropriate use casesPreferred Qualifications- Knowledge of natural language processing techniques and text classification systems- Background in building ML systems for talent acquisition, recruiting, or HR technology- Experience with real-time ML inference and low-latency model serving- Understanding of distributed computing and large-scale data processing