AI/ML Engineering Manager
Tasks
- Conduct technical assessments and interviews
- Drive ML operations and production monitoring
- Drive end to end ML solution design
- Hire and build ML team
- Lead ML assessments across infrastructure data and model lifecycle
- Lead and manage ML engineers
- Maintain technical relationship with customers
- Manage performance and career development
- Mentor engineers and build reference architectures
- Partner in pre sales scoping and proposals
- Provide structured feedback and coaching
- Shape ML architecture for client engagements
- Translate MLOps and LLMOps into production standards
Perks/Benefits
- 100% remote work
- Annual learning stipend
- Equipment stipend
- Flexible time off
- Office stipend
- Paid Certification Exams
- Peer bonus awards
- Phantom equity
- Professional development plan
- State of the art laptop and tools
Skills/Tech-stack
AWS | AWS Glue | Airflow | Amazon Bedrock | Amazon Kinesis | Amazon OpenSearch | Amazon SageMaker | Apache Kafka | Apache Spark | Bedrock Guardrails | Bedrock Knowledge Bases | Bias/fairness | CI/CD | CloudFormation | Dagster | Docker | Dspy | Feature Store | Hugging Face | Hugging Face Transformers | Inference Server | JAX | Knowledge bases | Kubeflow Pipelines | Kubernetes | LLM Evaluation | LLMOps | Langchain | Llamaindex | LoRA | ML Governance | MLOps | MLflow | MWAA | Model Registry | PEFT | PGVector | Pinecone | PySpark | PyTorch | QLoRA | Quantization | RAG | Responsible AI | Retrieval-Augmented Generation | SageMaker Clarify | SageMaker Feature Store | SageMaker Model Registry | Sagemaker Pipelines | Scikit-learn | TRL | TensorFlow | Terraform | Triton Inference | Triton Inference Server | VLLM | Vector Databases | Weaviate
Education
N/A
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