We’re seeking a hands-on AI/ML Engineer having 3-6 years of work experience with deep expertise in large language models, retrieval-augmented generation (RAG), and cloud-native ML development on AWS. You'll be a key driver in building scalable, intelligent learning systems powered by cutting-edge AI and robust AWS infrastructure.Core Skills & Technologies:LLM Ecosystem & APIsOpenAI, Anthropic, CohereHugging Face TransformersLangChain, LlamaIndex (RAG orchestration)Vector Databases & IndexingFAISS, Pinecone, WeaviateAWS-Native & ML ToolingAmazon SageMaker (training, deployment, pipelines)AWS Lambda (event-driven workflows)Amazon Bedrock (foundation model access)Amazon S3 (data lakes, model storage)AWS Step Functions (workflow orchestration)AWS API Gateway & IAM (secure ML endpoints)CloudWatch, Athena, DynamoDB (monitoring, analytics, structured storage)Languages & ML FrameworksPython (primary), PyTorch, TensorFlowNLP, RAG systems, embeddings, prompt engineeringWhat You’ll DoModel Development & TuningFine-tune and deploy LLMs and custom models using AWS SageMakerBuild RAG pipelines with LlamaIndex/LangChain and vector search enginesScalable AI InfrastructureArchitect distributed model training and inference pipelines on AWSDesign secure, efficient ML APIs with Lambda, API Gateway, and IAMProduct IntegrationEmbed intelligent systems (tutoring agents, recommendation engines) into learning platforms using Bedrock, SageMaker, and AWS-hosted endpointsRapid ExperimentationPrototype multimodal and few-shot learning workflows using AWS servicesAutomate experimentation and A/B testing with Step Functions and SageMaker PipelinesData & Impact AnalysisLeverage S3, Athena, and CloudWatch to define metrics and continuously optimize AI performanceCross-Team CollaborationWork closely with educators, designers, and engineers to deliver AI features that enhance student learningWho You Are:Deeply Technical: Strong foundation in machine learning, deep learning, and NLP/LLMsAWS-Fluent: Extensive experience with AWS ML services (especially SageMaker, Lambda, and Bedrock)Product-Minded: You care about user experience and turning ML into real-world valueStartup-Savvy: Comfortable with ambiguity, fast iterations, and wearing many hatsMission-Aligned: Passionate about education, human learning, and AI for goodBonus Points:Hands-on experience fine-tuning LLMs or building agentic systems using AWSOpen-source contributions in AI/ML or NLP communitiesFamiliarity with AWS security best practices (IAM, VPC, private endpoints)
Job Title
AI/ML Engineer — NLP, LLMs & RAG Systems