Lead ML & AI Engineer
Tasks
- Architect multimodal AI solutions using imagery and text
- Architect retrieval augmented generation architectures
- Build autonomous agent workflows
- Calibrate model confidence
- Define AI first platform technical architecture using foundation models
- Define success metrics for AI solutions
- Deliver scalable explainable responsible AI systems
- Design large language model applications
- Develop embedding models and vector search solutions
- Establish agentic AI orchestration best practices
- Evaluate emerging AI technologies
- Evaluate production ML systems end to end
- Guide AI technology adoption decisions
- Implement human-in-the-loop workflows
- Implement memory management for agents
- Integrate tools into agent workflows
- Lead cross-functional Agile teams
- Lead design and implementation of representation learning systems
- Mentor engineers and data scientists
- Process structured data for AI decision support
Perks/Benefits
- N/A
Skills/Tech-stack
Adversarial Networks | Agentic AI | Autogen | Autonomous Agents | BERT | Classification | Confidence Calibration | Convolutional Neural Networks | CrewAI | Deep learning | Diffusion Models | Distributed Systems | Embedding Models | Forecasting | Foundation Models | GRU | Generative Adversarial Networks | Geospatial Data | Human-in-the-loop | LSTM | Langgraph | Language Models | Large Language Models | Machine Learning | Memory Management | Model Interpretability | Multimodal AI | Neural Networks | Orchestration | PyTorch | Python | Regression | Representation Learning | Retrieval-Augmented Generation | Scikit-learn | Spatial indexing | TensorFlow | The Loop | Tool integration | Transformers | Vector Search | Vision Language Models | Vision-language
Education
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