ML Engineering Lead

Role & responsibilities

• Lead the design, development, deployment, and maintenance of production-grade machine learning systems.
• Provide technical leadership to ML Engineers, Data Scientists, and AI Engineers across the ML lifecycle.
• Translate business and product requirements into scalable ML solutions and technical architectures.
• Develop and productionize models for prediction, classification, recommendation, NLP, computer vision, and Generative AI use cases.
• Design and implement robust ML pipelines for data preparation, feature engineering, training, validation, deployment, and monitoring.
• Establish and improve MLOps practices, including CI/CD, model versioning, experiment tracking, model serving, and monitoring.
• Optimize models and ML infrastructure for accuracy, latency, scalability, reliability, and cost efficiency.
• Design scalable model-serving and inference architectures for batch and real-time applications.
• Evaluate and integrate LLMs, foundation models, RAG, vector databases, and AI-agent technologies where appropriate.
• Collaborate with Data Engineering, Software Engineering, Product, DevOps, and Data Science teams.
• Conduct technical design reviews, code reviews, architecture reviews, and establish engineering best practices.
• Troubleshoot production ML issues and lead root-cause analysis and resolution.
• Define technical roadmaps, estimate engineering effort, prioritize initiatives, and ensure timely delivery.
• Mentor engineers and promote best practices in ML engineering, software development, testing, and documentation.
• Ensure ML systems comply with requirements for data privacy, security, model governance, fairness, and responsible AI.
• Research emerging ML technologies and identify opportunities to improve existing products and platforms.

Preferred candidate profile

• 5 to 10 years of experience in Machine Learning Engineering, AI Engineering, Data Science, or related technical roles, with demonstrated technical leadership.
• Strong hands‑on expertise in Python, Machine Learning, Deep Learning, and production ML systems.
• Strong proficiency in PyTorch, TensorFlow, Scikit-learn, Pandas, NumPy, or equivalent ML frameworks.
• Experience with Generative AI, LLMs, RAG, embeddings, vector databases, prompt engineering, fine-tuning, and model evaluation is highly desirable.
• Strong understanding of MLOps, including MLflow/Kubeflow, experiment tracking, model versioning, deployment, monitoring, and CI/CD.
• Experience deploying ML solutions on AWS, Azure, or GCP.
• Proficiency with Docker, Kubernetes, Git, Terraform, APIs, microservices, and cloud infrastructure.
• Strong understanding of SQL, data pipelines, feature engineering, feature stores, distributed systems, and system design.
• Experience with real-time and batch inference, model serving, model optimization, and production monitoring.
• Demonstrated ability to take ML solutions from research/PoC to reliable production systems.
• Strong knowledge of software engineering principles, testing, code quality, scalability, and performance optimization.
• Experience mentoring engineers and leading cross‑functional technical initiatives.
• Strong problem‑solving, communication, stakeholder management, and technical decision‑making skills.
• Ability to balance model performance, engineering complexity, infrastructure cost, scalability, and business requirements.
• Bachelor's degree in Computer Science, AI/ML, Data Science, Engineering, Mathematics, Statistics, or a related field; Master's degree preferred.
• Experience with responsible AI, model governance, security, and data privacy is an advantage.