Azure ML

The AWS AI/ML Engineer is responsible for designing, building, deploying, and scaling AI and Machine Learning solutions on AWS. The role focuses on developing ML models, data pipelines, and production-grade inference systems using AWS-managed services, while ensuring scalability, security, and operational excellence.
This role supports predictive analytics, computer vision, NLP, and emerging GenAI-enabled ML use cases across enterprise environments.

Key Responsibilities

AI/ML Solution Development

• Design and develop machine learning models for classification, regression, forecasting, NLP, or computer vision use cases
• Build end-to-end ML pipelines (data ingestion, training, validation, deployment) on AWS
• Develop and deploy real-time and batch inference services
• Apply feature engineering, model tuning, and evaluation techniques
• Use Amazon SageMaker for training, tuning, deployment, and monitoring of ML models
• Design scalable architectures using AWS Lambda, ECS/EKS, Step Functions
• Manage data storage and access using S3, DynamoDB, RDS/Aurora
• Ensure availability, performance, and cost optimization of ML workloads
• Implement CI/CD pipelines for ML models and data workflows
• Enable model versioning, monitoring, retraining, and rollback
• Track model performance, drift, and data quality
• Follow best practices for MLOps, automation, and observability
• Adhere to enterprise security, privacy, and compliance standards
• Work closely with data engineers, cloud architects, and business stakeholders
• Translate business problems into AI/ML solutions
• Support POCs, pilots, and production rollouts
• Contribute to reusable ML frameworks and accelerators

Required Skills & Skill Set

Machine Learning & AI

• Strong understanding of supervised and unsupervised ML algorithms
• Experience with NLP, Computer Vision, or time-series models
• Feature engineering, hyperparameter tuning, model evaluation
• Amazon SageMaker (Studio, Pipelines, Endpoints, Model Monitor)
• Experience with data pipelines on AWS
• Strong proficiency in Python
• Hands-on with scikit-learn, TensorFlow, PyTorch
• REST APIs, inference services using FastAPI / Flask
• SQL and basic data engineering skills
• Data ingestion and transformation pipelines
• S3, Athena, Glue, Redshift (exposure preferred)
• Structured and unstructured data handling

Nice-to-Have Skills

• Exposure to Generative AI / LLM-based ML workflows
• Experience with Terraform or CloudFormation
• Knowledge of automation / RPA integrations
• Domain exposure to manufacturing, supply chain, or enterprise IT