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