Job description

The AI Machine Learning Engineer - MLOps & Model Deployment role is responsible for translating business problems into machine learning formulations and designing end-to-end AI solutions including feature extraction, training pipelines, hyperparameter tuning, and packaging models as reproducible artifacts. The role involves selecting suitable architectures such as gradient boosting and transformers, optimizing inference using quantization, distillation, and mixed precision, and conducting evaluation using calibration, fairness metrics, cost-sensitive metrics, and PR/ROC under imbalance. The position includes building real-time and batch inference services, integrating with message buses and vector databases, and implementing model versioning, lineage tracking, experiment tracking, rollbacks, and canary releases. The role requires monitoring schema checks, data drift, performance regression, and cost observability, and implementing alerting and autoscaling policies aligned with SLAs. The engineer designs data contracts, builds ETL/ELT pipelines using Spark and Databricks, enforces data quality gates, and manages schema evolution. The role applies privacy-by-design practices including PII handling, tokenization, and secure secrets management, and supports cost-efficient data architectures using tiering, caching, and Parquet or Delta formats. The AI ML Engineer also designs experiments including A/B testing and counterfactual evaluation, integrates models via APIs and SDKs with business rules and fallbacks, produces documentation including model cards and decision logs, and collaborates with stakeholders to present trade-offs and technical solutions. The position requires working with cloud platforms including Azure, AWS, and GCP, implementing scalable architectures, deploying machine learning models, and supporting production-grade AI systems.