Artificial Intelligence Engineer

7 days ago

bangalore, India Ironbook AI Full time

Job Description: AI Engineer (AWS AI Stack)Location: Remote (India) Experience: 5+ Years Availability: Immediate Joiners Preferred Employment Type: Full-timeAbout the RoleWe 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 ResponsibilitiesAI/ML Solution DevelopmentBuild, 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 & MLOpsBuild 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 & IntegrationIntegrate 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 & OptimizationArchitect 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 & ExperienceCore AI/ML Skills5+ 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.ProgrammingStrong in Python (mandatory)Experience with FastAPI, microservices, containerized ML workloadsExperience with Git, Docker, CI/CD pipelinesData EngineeringGood understanding of data modeling, ETL/ELT conceptsExperience with Glue, Athena, Kinesis, Redshift, or equivalentCloud & DevOpsStrong hands-on with:LambdaECS/EKS (nice to have)API GatewayCloudWatchIAM

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