Senior AI Engineer for Cloud-Native Systems

2 days ago


kannur, India beBeeAIENGINEER Full time

Job Title:Artificial Intelligence Engineer PositionAbout the Role:This is a senior-level AI engineering position that requires 5+ years of experience in designing, building, and deploying artificial intelligence systems. The ideal candidate should have strong expertise in the AWS AI/ML ecosystem, programming skills in Python, and experience with FastAPI, microservices, containerized ML workloads, and Git, Docker, CI/CD pipelines.The role involves working on various projects such as machine learning model development, deployment, and optimization using SageMaker, Bedrock, Lambda, EC2, ECR, and Step Functions. It also includes developing end-to-end ML pipelines (training, evaluation, deployment, monitoring), implementing vector search, embeddings pipelines, and LLM-based applications using Amazon Bedrock or open-source models.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 & Qualifications: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.The ideal candidate will be able to architect and deploy AI workloads efficiently, ensuring high performance and cost effectiveness while maintaining security and compliance standards. This is a challenging yet rewarding opportunity for experienced AI engineers to grow their careers and contribute to innovative projects at scale.



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