Job Title

We’re seeking a hands-on AI/ML Engineer 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.If you’re passionate about combining NLP, deep learning, and real-world application at scale—this is the role for you. 6-10 years of specialized experience in AI/ML is required.Core Skills & TechnologiesLLM 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 & TuningDesigns architecture for complex AI systems and makes strategic technical decisionsEvaluates and selects appropriate frameworks, techniques, and approachesFine-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 IntegrationLeads development of novel solutions to challenging problemsEmbed 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 learningMentors junior engineers and provides technical leadershipWho You AreDeeply 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 PointsHands-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)