AI Development Specialist

1 week ago

kollam, India beBeeEngineer Full time

Job Description:A highly skilled AI Engineer is needed to design, build, and deploy AI systems.Location: Remote (India)Experience: 5+ YearsAvailability: Immediate Joiners PreferredEmployment Type: Full-timeWe are seeking a talented individual with strong hands-on experience across the AWS AI/ML ecosystem. The ideal candidate will collaborate with cross-functional teams to create scalable, production-grade solutions using modern AWS-native tooling.Key ResponsibilitiesBuild, deploy, and optimize machine learning models on AWS using SageMaker, Bedrock, Lambda, EC2, ECR, and Step Functions.Develop end-to-end ML pipelines for training, evaluation, deployment, and monitoring.Implement vector search, embeddings pipelines, and LLM-based applications using Amazon Bedrock or open-source models.Design and develop 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 & ExperienceThe ideal candidate will have: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.ProgrammingStrong in Python (mandatory).Experience with FastAPI, microservices, containerized ML workloads.Experience with Git, Docker, CI/CD pipelines.Data EngineeringGood understanding of data modeling, ETL/ELT concepts.Experience with Glue, Athena, Kinesis, Redshift, or equivalent.Cloud & DevOpsStrong hands-on with:Lambda.ECS/EKS (nice to have).API Gateway.CloudWatch.IAM.

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