AI/ML Engineering Manager
Tasks
- Advise customers on MLOps and LLMOps operations
- Drive pre sales technical scoping and proposals
- Lead ML team hiring and development
- Lead client engagements from kickoff to delivery
- Manage team performance and career growth
- Mentor engineers and build reference architectures
- Oversee ML assessments for customer environments
- Provide technical authority for architect level outcomes
- Run technical assessments and set technical bar for ML roles
- Shape end to end ML system architecture
Perks/Benefits
- Equipment and office stipend
- Flexible PTO
- Fully remote
- Learning and development stipend
- Medical insurance
- Paid Certification Exams
- Peer bonus awards
- Phantom equity
- Professional development plan
- State of the art laptop
- Worldwide team culture
Skills/Tech-stack
AWS Bedrock | AWS CDK | AWS CloudFormation | AWS Lambda | AWS SageMaker | Airflow | Amazon Comprehend | Amazon Kinesis | Amazon Neptune | Amazon OpenSearch | Amazon Personalize | Amazon Rekognition | Amazon S3 | Amazon Textract | Amazon Transcribe | Amazon Translate | Amazon Web Services | Apache Hudi | Apache Iceberg | Apache Kafka | Apache Spark | Bias and fairness | CI/CD | DPO | Dagster | Delta Lake | Docker | HuggingFace PEFT | HuggingFace Transformers | Inference Server | JAX | Kinesis Video Streams | Kubeflow Pipelines | Kubernetes | Langchain | Llamaindex | LoRA | MLflow | MWAA | Neptune | PGVector | Pinecone | PySpark | PyTorch | QbQRA | Quantization | RAG | RLHF | Responsible AI | SGLang | Scikit-learn | TGI | TensorFlow | Terraform | Triton Inference | Triton Inference Server | VLLM | Vector Databases | Weaviate | Web Services | Weights and Biases | XGBoost
Education
N/A
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