Principal Engineer AI/ML
Tasks
- Architect multimodal grounded content generation systems
- Build end to end AI systems with LLMs and VLMs
- Design multimodal evaluation systems
- Develop retrieval and recommendation models
- Ensure UX privacy legal and security compliance
- Evaluate trade offs across cost latency and accuracy
- Implement LLM judge and ensemble evaluation
- Mentor engineers and elevate technical standards
- Run A B testing and human in the loop validation
- Shape AI ML technical vision and roadmap
- Translate models into production ready services
Perks/Benefits
- N/A
Skills/Tech-stack
A/B | A/B Testing | Agent Orchestration | B testing | CUDA | Computer Vision | Contrastive Learning | Data Drift | Data Quality | Data labeling | Dataset Management | Deep learning | Dense Representations | Distributed Training | Enterprise Search | Experiment platforms | Feature Stores | Fine Tuning | Generative AI | GraphRAG | Hugging Face | Hybrid Representations | JAX | Keras | LLMOps | Language Models | Language Processing | Large Language Models | Learning-to-Rank | LoRA | MLOps | Metric Learning | Model Serving | Multi-Agent | Multi-agent orchestration | Natural Language | Natural Language Processing | Observability | Prompt engineering | PyTorch | Python | QLoRA | RAG | Reinforcement Learning | Retrieval-Augmented Generation | Sparse representations | TensorFlow | Vector Databases | Vision Language Models | Vision-language
Education
Roles
AI | AI/ML Engineer | Engineer | Learning Engineer | ML Engineer | Machine Learning Engineer | Principal | Principal Engineer
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