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

- 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, 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

📌 Azure ML (Hyderabad)
🏢 Tata Consultancy Services
📍 Hyderabad