Job Description
Description
AWS ML stack: SageMaker (training, fine-tuning, endpoints), Bedrock (foundation models, embeddings, agents), Step Functions, Lambda, S3, DynamoDB.
Production ML: end-to-end pipelines, model fine-tuning (LoRA, PEFT), evaluation frameworks, MLOps (model registry, CI/CD, monitoring, drift detection).
RAG and embeddings: vector databases (OpenSearch Serverless, FAISS), chunking strategies, retrieval evaluation, knowledge base architectures.
Python (core), with working knowledge of ML frameworks (PyTorch, HuggingFace Transformers) and data tooling (pandas, numpy, PySpark).
Spatial/geometric ML: constraint optimization, generative and parametric design, layout algorithms, spatial reasoning.
LLM integration: prompt engineering, function calling, agentic architectures, NLP for domain-specific tasks (code compliance, design narration).
Software engineering fundamentals: APIs, containerisation (ECS/Docker), IaC (CDK or CloudFormation), CI/CD.