Job Description :

Key Responsibilities :

- Design, develop, and deploy AI/ML solutions using AWS cloud services.

- Build and optimize machine learning models for predictive analytics, recommendation systems, NLP, computer vision, and generative AI use cases.

- Develop and integrate AI-powered applications using AWS services such as Amazon SageMaker, Bedrock, Comprehend, Rekognition, Textract, and Lex.

- Implement Retrieval-Augmented Generation (RAG) architectures using Large Language Models (LLMs).

- Fine-tune and deploy foundation models on AWS Bedrock and SageMaker.

- Develop scalable APIs and microservices to expose AI functionalities.

- Implement MLOps pipelines for model training, deployment, monitoring, and governance.

- Integrate AI solutions with enterprise applications, databases, and cloud services.

- Monitor model performance, drift, and operational metrics in production environments.

- Ensure adherence to security, compliance, and responsible AI best practices.

- Collaborate with cross-functional teams to understand business requirements and translate them into AI solutions.

- Stay current with advancements in AI, Generative AI, AWS services, and industry trends.

Technical Skills :

- Strong proficiency in Python programming.

- Experience with Machine Learning frameworks: TensorFlow, PyTorch, Scikit-learn, Hugging Face Transformers.

- Hands-on experience with AWS AI/ML services: Amazon SageMaker, Amazon Bedrock, Amazon Comprehend, Amazon Rekognition, Amazon Textract, Amazon Lex, Amazon Kendra.

- Experience with Generative AI and LLM technologies (Prompt Engineering, Fine-Tuning, RAG, Vector Databases, AI Agents).

- Experience with cloud-native architectures, serverless computing, and AWS services (Lambda, API Gateway, ECS/EKS, EC2, S3, DynamoDB, RDS, CloudWatch, IAM).

- Knowledge of MLOps tools, CI/CD pipelines, Docker, Kubernetes, and data engineering/ETL pipelines.