AI Engineering Manager
Tasks
- Architect AI/ML systems
- Automate retraining pipelines
- Build agentic AI multi agent orchestration
- Build deep learning neural networks
- Build reusable AI frameworks and tooling
- Create AI roadmaps
- Define AI product vision
- Deploy models using Docker
- Deploy using API based deployments
- Design predictive analytics models
- Develop GenAI foundation model pipelines
- Develop NLP solutions
- Develop computer vision solutions
- Ensure ethical AI compliance
- Ensure model explainability
- Ensure model fairness
- Establish AI success metrics
- Establish responsible AI governance
- Evaluate LLM performance
- Fine-tune foundation models
- Identify AI opportunities
- Implement MLOps and LLMOps
- Implement retrieval augmented generation systems
- Lead cross-functional AI programs
- Manage model versioning
- Mentor AI engineers and data scientists
- Monitor model drift
- Monitor system reliability
- Perform prompt engineering
- Promote knowledge sharing communities of practice
- Set up CI CD for ML
- Translate business needs into AI solutions
- Use observability for AI services
Perks/Benefits
- N/A
Skills/Tech-stack
API deployment | Agent Orchestration | Agentic AI | BigQuery | CI/CD | Cloud Storage | Cloud platform | Computer Vision | Dataflow | Deep learning | Docker | Ensemble learning | Fairness | Fine Tuning | Generative AI | Git | Google Cloud | Google Cloud Platform | Keras | LLM Evaluation | LLMOps | Language Processing | Learning algorithms | MLOps | Machine Learning | Machine learning algorithms | Model Explainability | Model versioning | Modular Design | Monitoring | Multi-Agent | Multi-agent orchestration | Natural Language | Natural Language Processing | Neural Networks | Observability | Prompt engineering | PyTorch | Python | Regression | Responsible AI | Retrieval-Augmented Generation | Simulation optimization | TensorFlow | Test automation | Vertex AI
Education
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