AI/ML Engineering Manager
Tasks
- Advise on MLOps and LLMOps implementation
- Drive pre sales technical scoping
- Lead ML assessments end to end
- Lead customer engagements end-to-end
- Manage performance and hiring decisions
- Mentor engineers and architects
- Provide architect level oversight
- Run structured 1 1s and provide feedback
- Shape ML architecture and technical direction
- Unblock engineering on complex problems
Perks/Benefits
- Equipment and office stipend
- Flexible PTO
- Learning and development stipend
- Medical insurance
- Paid exams and certifications
- Peer bonus awards
- Professional development plan
- Remote work
- Travel up to 25 percent
Skills/Tech-stack
AI Governance | AWQ | AWS CDK | AWS SageMaker | Agent systems | Agentic Systems | Airflow | Amazon Bedrock | Amazon Kinesis | Amazon OpenSearch | Amazon Web Services | Apache Hudi | Apache Iceberg | Apache Kafka | Apache Spark | Artificial Intelligence | Bias detection | BitsAndBytes | CI/CD | CloudFormation | DPO | Dagster | Data Engineering | Delta Lake | Distributed Training | Docker | Fairness | Feature Engineering | Fine Tuning | GPTQ | HuggingFace Transformers | Inference Server | Infrastructure as Code | JAX | Kubeflow Pipelines | Kubernetes | LLMOps | Langchain | Llamaindex | LoRA | MLOps | MLflow | MWAA | Machine Learning | Model Deployment | Model Monitoring | Model Training | Multi-Agent | Multi-Agent Systems | Neptune | PEFT | PGVector | Pinecone | PySpark | PyTorch | Q LoRA | Quantization | RAG | RLHF | Responsible AI | SageMaker Clarify | Scikit-learn | TensorFlow | Terraform | Triton Inference | Triton Inference Server | VLLM | Vector Search | Weaviate | Web Services | Weights and Biases | XGBoost | “as-code”
Education
N/A
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