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