layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

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

LAYIQ - Security AI / ML Engineer, Lead at Toyota

· Design and implement prompt engineering patterns for managed AI service integration
· Build training data pipelines from the security data lake — curating, labeling, and versioning datasets from real enterprise security telemetry
· Fine-tune models on organization-specific security data — alert triage, risk scoring, finding classification
· Implement the analyst feedback loop — capturing human corrections to continuously improve model accuracy
· Build model evaluation frameworks with rigorous metrics (F1, precision, recall, false positive rates) benchmarked against analyst agreement
· Design and implement a model routing layer — directing each task to the optimal model based on complexity, latency requirements, and cost
· Monitor models in production for drift, accuracy degradation, and emerging failure modes
· Implement centralized token usage monitoring for leadership visibility into AI consumption and cost control
· Collaborate with the Lead Engineer on agent architectures — multi-agent orchestration, tool use, and autonomous triage workflows
· Deploy and manage model inference endpoints across cloud ML services and container-based serving
· Build the analyst feedback loop: approval/rejection signals in dashboards feeding back into retraining pipelines
· What you bring
· 3+ years in applied ML/AI engineering (not research-only — production deployment required)
· Hands-on experience with LLM fine-tuning — LoRA, QLoRA, or full fine-tuning on domain-specific data
· Experience with cloud ML platforms (e.g., AWS SageMaker): training jobs, hyperparameter tuning, model registry, endpoint deployment
· PyTorch proficiency for model training and custom architecture
· Experience building evaluation pipelines — automated metrics, human evaluation protocols, A/B testing
· Understanding of transformer architectures and attention mechanisms (not just API calls)
· Python fluency with production engineering practices (testing, CI/CD, monitoring)
· Strong communication skills with the ability to explain model behavior and limitations to non-ML stakeholders
Added bonus if you have
· Experience with security or cybersecurity data — alert classification, threat detection, anomaly detection
· Familiarity with model serving at scale (vLLM, Triton Inference Server, Tensor RT optimization)
· Hugging Face Ecosystem Experience Model Hub, tokenizers, datasets library, PEFT
· Experience with RAG architecture and vector databases
· Background in multi-model routing or mixture-of-experts approaches
· Understanding of agentic AI patterns — tool use, chain-of-thought, multi-step reasoning
· Experience with model cost optimization — quantization, distillation, caching strategies
· Self-motivated individual who thrives in ambiguous environments and can build processes from the ground up

Security AI / ML Engineer, Lead

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