Overview

𝗝𝗼𝗯 𝗧𝗶𝘁𝗹𝗲: Senior AI/ML Engineer

Responsibilities

• Design, develop, and deploy machine learning models for predictive analytics, optimization, and intelligent automation use cases.
• Build scalable and production-ready ML workflows using Amazon SageMaker.
• Develop end-to-end machine learning pipelines, including:
• Automate model lifecycle management using Amazon SageMaker Pipelines and MLOps best practices.
• Integrate machine learning models into backend services, APIs, and distributed systems.
• Monitor model performance, detect model drift, and implement retraining strategies to maintain accuracy and reliability.
• Collaborate with software engineering and platform teams to ensure ML systems are scalable, secure, and operationally efficient.
• Improve deployment, monitoring, and observability of machine learning infrastructure.
• Participate in architecture discussions and contribute to AI/ML platform design decisions.
• Mentor engineers and share best practices across machine learning engineering initiatives.
• Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, Engineering, or a related field.
• 8+ years of overall software engineering experience.
• 3+ years of hands-on experience designing and deploying machine learning models in production environments.
• Strong programming proficiency in Python.

Qualifications

• Hands-on experience deploying and managing machine learning workloads using Amazon SageMaker or equivalent cloud-based ML platforms.
• Strong understanding of:
• Strong analytical, debugging, and problem-solving skills.
• Ability to collaborate effectively with cross-functional engineering and product teams.
• Exposure to MLOps practices and ML lifecycle management tools.
• Knowledge of advanced AI techniques, including:
• Familiarity with cloud-based data engineering and modern deployment architectures.
• Python
• Scikit-learn
• XGBoost
• TensorFlow
• LightGBM
• Amazon SageMaker
• SageMaker Pipelines
• Cloud-Native ML Deployment
• Model Lifecycle Management
• Predictive Modeling
• Feature Engineering
• Model Evaluation
• Model Deployment
• Model Monitoring
• Model Drift Detection
• Automated Retraining
• Large-Scale Data Systems
• Distributed Computing
• Production ML Pipelines
• Data Processing Workflows