Lead Machine Learning Engineer

Role Overview

Key Responsibilities

• Lead the design and development of machine learning models and ML-driven systems
• Own end-to-end ML pipelines, including data preparation, training, evaluation, deployment, and monitoring
• Translate business and product requirements into practical ML solutions
• Architect and maintain scalable, production-ready ML systems
• Establish ML engineering best practices, standards, and review processes
• Mentor and technically guide ML engineers and data scientists
• Collaborate with backend, platform, and DevOps teams on system integration and deployment
• Ensure model performance, reliability, security, and responsible AI practices
• Continuously evaluate and improve models using metrics, monitoring, and feedback loops

Machine Learning & Technical Scope

• Design and train models using supervised, unsupervised, and deep learning techniques
• Experience with frameworks such as PyTorch, TensorFlow, or equivalent
• Strong understanding of feature engineering, model evaluation, and experimentation
• Experience deploying models into production environments (APIs, batch, or streaming)
• Familiarity with MLOps practices, including CI/CD for ML, model versioning, and monitoring
• Experience working with large-scale datasets and distributed training where applicable

Required Qualifications & Experience

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or related field
• 7+ years of professional experience in machine learning or applied AI roles
• Strong programming skills in Python; familiarity with data processing libraries
• Proven experience deploying and maintaining ML models in production
• Solid understanding of software engineering principles and system design
• Experience working cross-functionally with product and engineering teams

Preferred Experience

• Experience leading or mentoring ML engineers
• Familiarity with cloud platforms (Azure preferred; AWS or GCP acceptable)
• Experience with containerization and orchestration (Docker, Kubernetes)
• Exposure to NLP, computer vision, recommender systems, or large language models
• Experience in regulated or production-critical environments