Job Summary

Traditional AI Developer AWS 710 years of hands-on experience in Artificial Intelligence Machine Learning Data Science or Applied AI roles. Strong background in Classical Machine Learning, Statistical Modeling, Predictive Analytics and Deep Learning. Experience applying classical ML algorithms to solve complex business problems. Experience in data preprocessing, feature engineering, feature selection and exploratory data analysis. Expertise in Deep Learning techniques including CNN, RNN, LSTM, Transformers, and Neural Networks. Experience building, validating, and optimizing predictive models using statistical methods and ML frameworks. Experience in model evaluation, hyperparameter tuning, performance optimization, and bias-variance analysis. Understanding of the complete AI Model Lifecycle: Training, Validation, Deployment, Monitoring. Solid experience developing scalable AI/ML pipelines for training, inference, and model serving. Hands-on experience with Amazon SageMaker, AWS Glue, Lambda, EMR, EC2, EKS and AWS analytics services. Experience integrating AI models with enterprise applications through APIs, microservices and cloud-native architectures. Collaborate with data engineers and business stakeholders to implement AI-driven solutions. Familiarity with MLOps practices including CI/CD, model versioning deployment automation and monitoring. Exposure to Spark, Hadoop, Docker, Kubernetes and distributed computing environments. Good communication and stakeholder management skills.

Requirements

- Traditional AI Developer AWS 710 years of hands-on experience in Artificial Intelligence Machine Learning Data Science or Applied AI roles
- Strong background in Classical Machine Learning,



Statistical Modeling, Predictive Analytics and Deep Learning
- Experience applying classical ML algorithms to solve complex business problems
- Experience in data preprocessing, feature engineering, feature selection and exploratory data analysis
- Expertise in Deep Learning techniques including CNN, RNN, LSTM, Transformers, and Neural Networks
- Experience building, validating and optimizing predictive models using statistical methods and ML frameworks
- Experience in model evaluation, hyperparameter tuning, performance optimization, and bias-variance analysis
- Understanding of the complete AI Model Lifecycle: Training, Validation, Deployment, Monitoring
- Strong experience developing scalable AI/ML pipelines for training, inference, and model serving
- Hands-on experience with Amazon SageMaker, AWS Glue, Lambda, EMR, EC2, EKS and AWS analytics services
- Experience integrating AI models with enterprise applications through APIs, microservices and cloud-native architectures
- Collaborate with data engineers and business stakeholders to implement AI-driven solutions
- Familiarity with MLOps practices including CI/CD, model versioning, deployment automation and monitoring
- Exposure to Spark, Hadoop, Docker, Kubernetes and distributed computing environments
- Good communication and stakeholder management skills

Key Skills

- AWS
- Amazon SageMaker
- CNN
- RNN
- LSTM
- Transformers
- AIML pipelines
- MLOps
- Spark

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📌 Specialist - Data Sciences (Bengaluru)
🏢 LTM
📍 Bengaluru