layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

LAYIQ - Lead ML/AI Engineer at Toyota

Bachelor's degree in Computer Science, Machine Learning, Statistics, or related field, or equivalent practical experience
5+ years of software engineering experience, including 2–4 years focused on ML/AI in production
Solid understanding of machine learning fundamentals : supervised and unsupervised learning, deep learning architectures (transformers, CNNs, RNNs), optimization techniques, and evaluation methodologies
Hands-on experience with large language models : prompt engineering, RAG pipelines, embedding models, vector databases, and agent frameworks (LangChain, LlamaIndex, or similar)
Experience working with AWS AI/ML services , including: Amazon Bedrock for foundation model access and knowledge bases, or Amazon SageMaker for model training, hosting, and MLOps pipelines Lambda and Step Functions for orchestrating inference workflows S3 for data storage and model artifact management EventBridge , SQS , or SNS for event-driven ML pipelines OpenSearch or similar for vector search and semantic retrieval
Strong proficiency in Python — you write production-quality ML code, not just notebooks
Experience with core ML frameworks: PyTorch , TensorFlow , or JAX , and libraries like Hugging Face Transformers, scikit-learn, and XGBoost
Familiarity with MLOps practices : experiment tracking (MLflow, W&B), model registries, and CI/CD for ML workflows
Experience with data engineering fundamentals: ETL pipelines, feature stores, data validation, and working with structured and unstructured data
Understanding of Infrastructure as Code using AWS CDK , CloudFormation, or Terraform for ML infrastructure
Experience with observability and monitoring for ML systems: model performance tracking, data drift detection, and alerting
Clear communicator — comfortable discussing model tradeoffs and technical decisions with teammates and stakeholders
Added bonus if you have
Master's degree in Machine Learning, AI, Computer Science, Statistics, or related field
Experience in the financial services, banking, or insurance industry
Experience with responsible AI : fairness metrics, bias detection, explainability (SHAP, LIME), and model governance
Familiarity with NLP beyond LLMs (named entity recognition, document understanding, OCR)
Exposure to real-time inference optimization: quantization, distillation, ONNX, or latency-sensitive serving
Experience with containerized ML workloads (ECS Fargate, Docker) for training and serving
Exposure to computer vision or multi-modal architectures combining text, image, and structured data
Familiarity with GraphQL or API gateway patterns for exposing ML services to backend consumers
AWS certifications (Machine Learning Specialty, Developer Associate, or Cloud Practitioner)
Experience contributing to open-source ML projects

Lead ML/AI Engineer

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