Machine Learning Engineer

Responsibilities:Machine Learning Development & Implementation (40%)Design and implement end-to-end ML pipelines for recommendation systems, search ranking, and classification problemsBuild and optimize traditional ML models using techniques such as ensemble methods, SVMs, gradient boosting, and neural networksDevelop time series forecasting models and ranking algorithms for complex business applicationsImplement feature engineering pipelines that handle real-world data noise and edge casesCreate 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 andalertingBuild MLOps pipelines using MLflow for experiment tracking and model registry managementDesign scalable data workflows using Apache Airflow and Azure Data Factory for ETL operationsEstablish 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 decisionsMentor team members on best practices for production ML system design and implementationCommunicate complex technical concepts clearly to both technical and non-technical stakeholdersCollaborate across AI, web development, and system architecture teams toensure seamless integrationGuide 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 methodsIntegrate generative AI tools effectively into workflows without over-relying on themDesign ML systems that integrate seamlessly with existing web application architecturesProvide technical guidance onmodel selection, evaluation metrics, and performance optimizationStay current with ML best practices while maintaining focus on practical, business-driven solutionsRequired QualificationsEducation & ExperienceBachelor's or Master's degree in Computer Science, Data Science, Statistics, or related technical field4+ years of hands-on experience building and deploying machine learning systems in productionProven experience working in non-technical business domains (healthcare, finance, retail, HR, etc.)Track record of mentoring technical team members and leading collaborative projectsCore Technical SkillsProgramming Excellence: Expert-level Python proficiency with focus on clean, maintainable, production-ready codeTraditional ML Expertise: Deep understanding of classification, regression, ranking, and recommendation algorithmsProduction ML: Experience with MLOps practices, model deployment, monitoring, and lifecycle managementData Engineering: Proficiency with data pipeline development, ETL processes, and handling messy real-world datasetsCloud 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 modelsBackground in A/B testing methodologies and measuring business impact of ML initiativesKnowledge of feature stores, model registry systems, and ML experiment trackingUnderstanding of model interpretability, bias detection, and fairness in ML systemsExperience with both structured and unstructured data processing at scaleExperience with deep learning frameworks (TensorFlow, PyTorch) for appropriate use casesPreferred QualificationsKnowledge of natural language processing techniques and text classification systemsBackground in building ML systems for talent acquisition, recruiting, or HR technologyExperience with real-time ML inference and low-latency model servingUnderstanding of distributed computing and large-scale data processing