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 & Technologies LLM Ecosystem & APIs OpenAI, Anthropic, Cohere Hugging Face Transformers LangChain, LlamaIndex (RAG orchestration) Vector Databases & Indexing FAISS, Pinecone, WeaviateAWS-Native & ML Tooling Amazon 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 Frameworks Python (primary), PyTorch, TensorFlow NLP, RAG systems, embeddings, prompt engineering What You’ll Do Model Development & Tuning Designs architecture for complex AI systems and makes strategic technical decisions Evaluates and selects appropriate frameworks, techniques, and approaches Fine-tune and deploy LLMs and custom models using AWS SageMaker Build RAG pipelines with LlamaIndex/LangChain and vector search engines Scalable AI Infrastructure Architect distributed model training and inference pipelines on AWS Design secure, efficient ML APIs with Lambda, API Gateway, and IAM Product Integration Leads development of novel solutions to challenging problems Embed intelligent systems (tutoring agents, recommendation engines) into learning platforms using Bedrock, SageMaker, and AWS-hosted endpoints Rapid Experimentation Prototype multimodal and few-shot learning workflows using AWS services Automate experimentation and A/B testing with Step Functions and SageMaker Pipelines Data & Impact Analysis Leverage S3, Athena, and CloudWatch to define metrics and continuously optimize AI performance Cross-Team Collaboration Work closely with educators, designers, and engineers to deliver AI features that enhance student learning Mentors junior engineers and provides technical leadership Who You Are Deeply Technical: Strong foundation in machine learning, deep learning, and NLP/LLMs AWS-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 value Startup-Savvy: Comfortable with ambiguity, fast iterations, and wearing many hats Mission-Aligned: Passionate about education, human learning, and AI for good Bonus Points Hands-on experience fine-tuning LLMs or building agentic systems using AWS Open-source contributions in AI/ML or NLP communities Familiarity with AWS security best practices (IAM, VPC, private endpoints)