Role & responsibilities

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 settings.

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
Robust 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, Endp

📌 Aws Ai/ml Engineer Bengaluru
🏢 Tata Consultancy Services
📍 Bengaluru