Lead Machine Learning Engineer

Role Overview

We are seeking a Lead Machine Learning Engineer to design, build, and scale production‑grade machine learning systems. This role combines deep hands‑on ML engineering with technical leadership, owning the end‑to‑end lifecycle of ML solutions from research and prototyping through deployment and monitoring.

The Lead ML Engineer will work closely with product, data, platform, and engineering teams to deliver robust, scalable, and responsible ML‑powered capabilities across the organization.

• 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 Dev Ops 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

• Design and train models using supervised, unsupervised, and deep learning techniques
• Experience with frameworks such as PyTorch, Tensor Flow, 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

• 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

• 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