Artificial Intelligence Engineer
18 hours ago
Job Description: AI Engineer (AWS AI Stack) Location: Remote (India) Experience: 5+ Years Availability: Immediate Joiners Preferred Employment Type: Full-time About the Role We are looking for a highly skilled AI Engineer with strong hands-on experience across the AWS AI/ML ecosystem . You will design, build, and deploy AI systems, collaborate with cross-functional teams, and contribute to scalable, production-grade solutions using modern AWS-native tooling. Key Responsibilities AI/ML Solution Development Build, deploy, and optimize machine learning models on AWS using SageMaker, Bedrock, Lambda, EC2, ECR, and Step Functions. Develop end-to-end ML pipelines (training, evaluation, deployment, monitoring). Implement vector search, embeddings pipelines, and LLM-based applications using Amazon Bedrock or open-source models. Build RAG (Retrieval-Augmented Generation) workflows using AWS services such as OpenSearch / Aurora / DynamoDB. Data Engineering & MLOps Build scalable data pipelines using Glue, EMR, Kinesis, or Lambda. Implement MLOps workflows using SageMaker Pipelines, Model Registry, MLflow (if applicable), and CI/CD. Monitor and optimize model performance, drift detection, retraining triggers. Backend & Integration Integrate models with applications via REST APIs / async APIs. Work with microservices using Python (FastAPI), Node.Js, or similar. Build inference endpoints optimized for low latency and cost efficiency. Cloud Architecture & Optimization Architect and deploy AI workloads following AWS Well-Architected best practices. Optimize compute, storage, and networking for high performance and cost efficiency. Implement security, IAM policies, data encryption, and compliance practices. Required Skills & Experience Core AI/ML Skills 5+ years of ML/AI engineering experience, preferably in production environments. Strong expertise with: AWS SageMaker (training, inference, Pipelines, Model Monitor, Debugger). Amazon Bedrock (LLMs, embeddings, fine-tuning or instruction tuning). Feature Store , SageMaker JumpStart , Batch Transform . Solid experience with deep learning frameworks: PyTorch , TensorFlow , Hugging Face , LangChain (optional but preferred). Experience building LLM agents, automation workflows, or RAG-based systems. Programming Strong in Python (mandatory) Experience with FastAPI, microservices, containerized ML workloads Experience with Git, Docker, CI/CD pipelines Data Engineering Good understanding of data modeling, ETL/ELT concepts Experience with Glue, Athena, Kinesis, Redshift, or equivalent Cloud & DevOps Strong hands-on with: Lambda ECS/EKS (nice to have) API Gateway CloudWatch IAM
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