Artificial Intelligence Architect

Machine Learning Engineer Job DescriptionWe are seeking a skilled Machine Learning Engineer to lead the design, implementation, and deployment of innovative AI solutions.Responsibilities:AI Solution Development: Design and implement end-to-end ML pipelines for recommendation systems, search ranking, and classification problems.Traditional ML Expertise: Build and optimize traditional ML models using techniques such as ensemble methods, SVMs, gradient boosting, and neural networks.Data Engineering: Develop time series forecasting models and ranking algorithms for complex business applications.Feature Engineering: Implement feature engineering pipelines that handle real-world data noise and edge cases.Model Reliability: Create robust data preprocessing and validation systems that ensure model reliability in production.Production ML Systems & Deployment:Docker Containerization: Deploy ML models using Docker containerization and REST API frameworks (Flask/FastAPI).Cloud Platforms: Implement model serving solutions on Azure Container Instances with proper monitoring and alerting.MLOps Pipelines: Build MLOps pipelines using MLflow for experiment tracking and model registry management.Technical Leadership & Collaboration:Technical Guidance: Serve as a technical sounding board for AI team members on ML architecture and approach decisions.Mentorship: Mentor team members on best practices for production ML system design and implementation.Communication: Communicate complex technical concepts clearly to both technical and non-technical stakeholders.Cross-Functional Collaboration: Collaborate across AI, web development, and system architecture teams to ensure seamless integration.Strategic ML Decision Making:Evaluation: Evaluate problems to determine optimal solutions: classical ML, GenAI, or simpler analytical methods.GenAI Integration: Integrate generative AI tools effectively into workflows without over-relying on them.ML System Design: Design ML systems that integrate seamlessly with existing web application architectures.Technical Guidance: Provide technical guidance on model selection, evaluation metrics, and performance optimization.Industry Trends: Stay current with ML best practices while maintaining focus on practical, business-driven solutions.Required Qualifications:Education & Experience:Bachelor's or Master's Degree: Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or related technical field.Hands-on Experience: 4+ years of hands-on experience building and deploying machine learning systems in production.Domain Experience: Proven experience working in non-technical business domains (healthcare, finance, retail, HR, etc.).Mentorship: 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:Recommendation Engines: Experience building recommendation engines, search ranking systems, or time series forecasting models.A/B Testing: Background in A/B testing methodologies and measuring business impact of ML initiatives.Feature Stores: Knowledge of feature stores, model registry systems, and ML experiment tracking.Model Interpretability: Understanding of model interpretability, bias detection, and fairness in ML systems.Distributed Computing: Experience with both structured and unstructured data processing at scale.Deep Learning Frameworks: Experience with deep learning frameworks (TensorFlow, PyTorch) for appropriate use cases.Preferred Qualifications:NLP Techniques: Knowledge of natural language processing techniques and text classification systems.HR Technology: Background in building ML systems for talent acquisition, recruiting, or HR technology.Real-time Inference: Experience with real-time ML inference and low-latency model serving.Distributed Computing: Understanding of distributed computing and large-scale data processing.],