AI/ML Engineering Manager
Tasks
- Build reference architectures and reusable ML accelerators
- Drive end to end ML assessments for customers
- Drive engagement architecture and solution design from kickoff to delivery
- Lead pre sales scoping and technical proposals
- Lead team hiring and technical assessments
- Manage ML engineer performance and development
- Mentor engineers through real work and interviews
- Provide architect level technical oversight for client engagements
- Set ML architecture and technical direction
- Translate MLOps and LLMOps into production standards
Perks/Benefits
- Equipment and office stipend
- Flexible time off
- Incredible corporate culture
- Learning and development stipend
- Paid exam and certification fees
- Paid travel up to 25 percent
- Peer bonus awards
- Professional development plan
- Remote work
- State of the art laptop and tools
- Worldwide Team Collaboration
Skills/Tech-stack
AWS CDK | AWS Certified | AWS Certified Machine Learning | AWS Certified Machine Learning – Specialty | AWS Certified Solutions Architect | AWS Certified Solutions Architect Professional | AWS Glue | Airflow | Amazon Kinesis | Amazon OpenSearch | Amazon Web Services | Apache Hudi | Apache Iceberg | Apache Kafka | Apache Spark | Artificial Intelligence | Bedrock | Bias detection | CI/CD | CloudFormation | DPO | Dagster | Delta Lake | Docker | Fairness | HuggingFace Transformers | Inference Optimization | Infrastructure as Code | JAX | Kubeflow Pipelines | Kubernetes | LLM Inference | LLM Inference Optimization | LLMOps | LoRA | MLOps | MLOps Governance | MLflow | MWAA | Machine Learning | Model Evaluation | PEFT | PGVector | Pinecone | PySpark | PyTorch | QLoRA | Quantization | RAG | RLHF | Responsible AI | SageMaker | Scikit-learn | TensorFlow | Terraform | Triton | VLLM | Vector Databases | Weaviate | Web Services | XGBoost | “as-code”
Education
N/A
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