Principal AI/ML Architect
Tasks
- Advise on MLOps standards and architecture
- Architect LLMOps patterns for enterprise AI systems
- Design production ML systems architecture
- Influence model lifecycle and monitoring frameworks
- Lead end to end ML assessments
- Orchestrate POCs for customer engagements
- Partner on sales proposals and scoping
- Perform rigorous model evaluation and safety reviews
- Provide senior technical authority and guidance
- Shape ML practice standards and reference architectures
- Translate ML tradeoffs into business risk and value
Perks/Benefits
- Equipment and office stipend
- Flexible time off
- Learning and development stipend
- Paid Certification Exams
- Paid tools and equipment
- Peer bonus awards
- Private health insurance
- Professional development plan
- Remote work
- State of the art laptop and tools
Skills/Tech-stack
AI Governance | AWS | AWS CDK | Agentic Systems | Airflow | Amazon Bedrock | Amazon Kinesis | Amazon OpenSearch | Amazon SageMaker | Apache Kafka | Apache Spark | Artificial Intelligence | Bias and fairness | CI/CD | CloudFormation | DPO | Deep learning | DeepSpeed | Distributed Training | Docker | FSDP | Feature Store | Fine Tuning | HuggingFace Transformers | Inference Server | Inferentia | Infrastructure as Code | Kubeflow Pipelines | Kubernetes | LLM Evaluation | LLMOps | Langchain | Llamaindex | LoRA | MLOps | MLflow | Machine Learning | Model Registry | PEFT | PGVector | Pinecone | PySpark | PyTorch | Q LoRA | Quantization | RAG | RLHF | Responsible AI | SGLang | SageMaker Clarify | SageMaker Feature Store | SageMaker Model Registry | Scikit-learn | TensorFlow | Terraform | Trainium | Triton Inference | Triton Inference Server | VLLM | Vector Databases | Weaviate | Weights and Biases | “as-code”
Education
N/A
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