Machine Learning Engineer

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 production Production 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 and alerting - 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 environments Technical 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 approaches Strategic 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 solutions Required Qualifications Education & 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 projects Core 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 Factory Specialized 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 cases Preferred 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