AI ML Engineer

Role Overview

AI ML Engineer at Apparel Group.

Role Purpose

Design, build, and deploy end-to-end machine learning solutions that translate business problems into production systems. Own the full lifecycle from model formulation through inference optimization, data quality, and stakeholder enablement.

Key Responsibilities

Model & Solution Engineering

• Translate business problems into machine learning formulations with clear success metrics.
• Select suitable model architectures including gradient boosting, transformers, and other approaches.
• Build end-to-end pipelines covering feature extraction, training, hyperparameter tuning, and model packaging as reproducible artifacts.
• Optimize inference for latency and throughput on CPU/GPU using quantization, distillation, and mixed precision techniques.
• Conduct evaluation beyond accuracy, including calibration, fairness, cost-sensitive metrics, and PR/ROC analysis under class imbalance.

MLOps, Deployment & Observability

• Implement model versioning, lineage tracking, and experiment tracking systems.
• Manage model rollbacks and canary releases.
• Build real-time and batch inference services integrated with message buses and vector databases.
• Monitor for schema checks, data drift, performance regression, and cost observability.
• Create alerting and autoscaling policies tied to service-level agreements.
• Maintain incident runbooks for model services.

Data Engineering, Quality & Governance

• Design data contracts and implement ETL/ELT pipelines using Spark and Databricks.
• Implement testing and backfills within data pipelines.
• Enforce data quality gates and schema evolution strategies to prevent mismatches.
• Apply privacy-by-design principles including PII handling, tokenization, and secure secrets management.
• Collaborate on cost-efficient data architectures using tiering, caching, Parquet, and Delta formats.

Experimentation, Product Integration & Stakeholder Enablement

• Design experiments including A/B testing and counterfactual evaluation.
• Define guardrails and success criteria with product teams.
• Integrate models via APIs and SDKs with business rules and fallbacks for graceful degradation.
• Produce clear documentation including model cards and decision logs.
• Present trade-offs and technical concepts to stakeholders.

Qualifications & Experience

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence/Machine Learning, or a related field.
• Proven experience designing, training, and deploying machine learning models and AI solutions.
• Strong programming skills in Python.
• Familiarity with machine learning frameworks including TensorFlow, PyTorch, and Scikit-learn.
• Hands-on experience with MLOps tools and practices including Docker, Kubernetes, MLflow, and CI/CD pipelines.
• Proficiency in data processing and ETL tools including Spark and Databricks.
• Experience working with large datasets.
• Knowledge of model optimization techniques including quantization and distillation.
• Experience with performance tuning for production environments.
• Familiarity with cloud platforms including Azure, AWS, or GCP.
• Knowledge of scalable architecture design.
• Understanding of data governance, privacy standards, and compliance requirements.

Skills & Competencies

• Strong analytical and problem-solving skills with attention to detail.
• Excellent communication skills to collaborate with cross-functional teams.
• Ability to present technical concepts clearly.