AI ML Engineer

About the role

The AI ML Engineer will translate business problems into machine‑learning solutions, build end‑to‑end pipelines, and ensure models are production‑ready, scalable, and compliant with data governance standards.

Key responsibilities

• Design and implement ML models (gradient boosting, transformers) with clear success metrics and robust evaluation beyond accuracy.
• Build end‑to‑end pipelines for feature extraction, training, hyperparameter tuning, and model packaging.
• Optimize inference using quantization, distillation, and mixed‑precision techniques for low latency on CPU/GPU.
• Implement MLOps practices: model versioning, experiment tracking, canary releases, and rollback procedures.
• Develop real‑time and batch inference services, integrate with message buses and vector databases.
• Monitor data drift, performance regression, and cost; create alerting and autoscaling policies tied to SLAs.
• Design data contracts, ETL/ELT pipelines (Spark/Databricks), and enforce data quality gates.
• Apply privacy‑by‑design principles for PII handling, tokenization, and secure secrets management.
• Design experiments (A/B, counterfactual) and define guardrails with product teams.
• Produce documentation such as model cards and decision logs, and present trade‑offs to stakeholders.

Required profile

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI/ML, or a related field.
• Proven experience designing, training, and deploying machine‑learning models and AI solutions.
• Strong analytical and problem‑solving abilities with attention to detail.
• Excellent communication skills for cross‑functional collaboration.

Required skills

• Python programming.
• ML frameworks: TensorFlow, PyTorch, Scikit‑learn.
• MLOps tools: Docker, Kubernetes, MLflow, CI/CD pipelines.
• Data processing: Spark, Databricks.
• Model optimization: quantization, distillation.
• Cloud platforms: Azure, AWS, GCP.
• Understanding of data governance, privacy standards, and compliance.