AIML Engineer / Lead

Key responsibilities

• Translate business problems into measurable machine-learning objectives and evaluation criteria.
• Perform exploratory analysis, data preprocessing, feature engineering and feature selection.
• Develop and compare supervised, unsupervised and deep-learning models as appropriate.
• Execute hyperparameter tuning, cross-validation, error analysis and model explainability.
• Build reusable training, validation and inference pipelines in Python.
• Deploy batch and real-time inference services using APIs or managed cloud endpoints.
• Monitor model quality, drift, latency, reliability and cost after deployment.
• Collaborate with data engineers to define reliable data inputs and feature pipelines.
• Apply engineering standards for testing, versioning, documentation and reproducibility.
• Contribute to architecture decisions for scalable and secure AI/ML systems.

Required qualifications and experience

• 5+ years of relevant experience in machine learning, applied data science or AI engineering.
• Strong Python, data manipulation and machine-learning implementation skills.
• Demonstrable experience taking models from experimentation to production.
• Experience with at least one cloud platform: AWS, Microsoft Azure or Google Cloud Platform.
• Strong understanding of model evaluation, statistical reasoning and business-aligned metrics.
• Ability to communicate trade-offs among model quality, latency, scalability and cost.