Principal Machine Learning Engineer
Tasks
- Architect scalable production ML pipelines
- Build Retrieval Augmented Generation systems
- Collaborate cross functionally on enterprise requirements
- Design evaluation loops
- Drive bias aware development
- Guide retrieval indices and embedding pipelines
- Implement model training and fine tuning pipelines
- Improve observability reproducibility versioning testing
- Integrate agent frameworks and RAG systems
- Lead GenAI and agentic ML systems
- Mentor senior engineers and researchers
- Monitor model performance and diagnose issues
- Optimize accuracy latency cost interpretability
- Orchestrate AI agents
- Prototype and deploy multimodal and long context models
- Research and integrate ML and LLM advancements
- Scale RAG and ML agents to large knowledge sets
Perks/Benefits
Skills/Tech-stack
Agent Orchestration | Batching | C++ | Deep learning | Distributed Training | Embeddings | Evaluation | Fine Tuning | Information Retrieval | Java | Language Models | Large Language Models | Logging | Machine Learning | Model Fine-tuning | Model Serving | Model Training | Model versioning | Monitoring | Pruning | PyTorch | Python | Quantization | RAG | Retrieval-Augmented Generation | Search | TensorFlow
Education
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