Senior AI/ML Engineering Lead

- Bachelors degree in Computer Science, Machine Learning, Statistics, or a related field, or equivalent practical experience - 7+ years of software engineering experience, including 3-5 years specifically in production ML/AI, demonstrating performance at a principal or staff engineer level - Profound understanding of machine learning principles: supervised and unsupervised learning, deep learning architectures (transformers, CNNs, RNNs), optimization techniques, and evaluation methodologies - Practical experience with large language models: prompt engineering, fine-tuning (LoRA, QLoRA), RAG pipelines, embedding models, and agent frameworks (LangChain, LlamaIndex, or similar) - Production-level experience with AWS AI/ML services, including Amazon Bedrock, Amazon SageMaker, Lambda, Step Functions, S3, EventBridge, SQS, SNS, and OpenSearch or similar technologies - Strong proficiency in Python or Typescript, capable of writing production-ready ML code - Familiarity with fundamental ML frameworks: PyTorch, TensorFlow, or JAX, and libraries like Hugging Face Transformers, scikit-learn, and XGBoost - Solid understanding of MLOps practices: experiment tracking (MLflow, W&B), model registries, CI/CD for ML, A/B testing, and canary deployments - Experience with data engineering essentials: ETL pipelines, feature stores, data validation, and working with structured and unstructured data at scale - Strong grasp of Infrastructure as Code using AWS CDK, CloudFormation, or Terraform for ML infrastructure - Experience with monitoring and observability for ML systems: tracking model performance, identifying data drift, and alert mechanisms - Extensive experience debugging complex issues in ML systems, from training instabilities to inference latency to data pipeline failures - Exceptional written and verbal communication skills, capable of drafting clear RFCs, leading design reviews, and conveying model tradeoffs to non-technical stakeholders