About the Job We are seeking a highly skilled AI/ML Engineer with expertise in AWS AI/ML services and a strong understanding of Generative AI using Amazon Bedrock. The ideal candidate will have experience in building, deploying, and optimizing AI/ML models on AWS, integrating LLMs into applications, and leveraging AWS services for scalable AI solutions. Experience Required - 4+ years Key Responsibilities Design, develop, and deploy AI/ML models on AWS, leveraging SageMaker, Bedrock, and related services. Build LLM-based applications using Amazon Bedrock and fine-tune models for specific use cases. Implement RAG (Retrieval-Augmented Generation) and integrate vector databases like OpenSearch, Pinecone, or FAISS. Develop scalable, production-ready ML pipelines using AWS services (Lambda, Step Functions, S3, DynamoDB, etc.). Utilize Bedrock, SageMaker, and custom fine-tuned models to deliver business-driven AI solutions. Work with cross-functional teams to integrate ML models into real-world applications. Ensure AI solutions adhere to best practices for security, compliance, and cost optimization. Stay updated with the latest trends in GenAI, prompt engineering, and AI model optimization. Required Skills Strong expertise in AWS AI/ML stack – Amazon Bedrock, SageMaker, Lambda, Step Functions, S3, DynamoDB, etc. Experience with Generative AI models (GPT, Claude, Mistral, LLaMA, etc.) and fine-tuning techniques. Hands-on experience in Python, TensorFlow, PyTorch, or Hugging Face. Knowledge of vector databases and embedding models. Experience in building secure and scalable AI applications using AWS. Familiarity with MLOps practices, CI/CD for ML models, and cloud automation. Strong problem-solving skills and ability to work in a fast-paced environment. Good to Have Experience with LangChain, Prompt Engineering, and RAG techniques. Understanding of data governance, AI ethics, and responsible AI practices. Certification in AWS Machine Learning Specialty/ Associate or relevant AI certifications. Relevant Skills vector databases, models, mlops, ml, pytorch, s3, lambda, tensorflow, ci/cd, amazon Show more Show less