Senior AI Engineer
Tasks
- Build multimodal AI systems
- Conduct adversarial stress testing
- Create data pipelines for structured and unstructured healthcare data
- Deploy models using cloud native tools
- Design retrieval-augmented generation solutions
- Develop synthetic data generation strategies
- Drive adoption of reproducibility validation and explainability
- Ensure HIPAA and GDPR compliant data handling
- Evaluate and integrate ML research and open source frameworks
- Fine-tune LLMs for healthcare use cases
- Implement model monitoring drift detection and auto retraining
- Implement speech to text and text to speech for conversational interfaces
- Lead end-to-end AI development
- Lead model evaluation and bias mitigation
- Manage documentation and version control for models and experiments
- Optimize models for accuracy, latency, and efficiency
- Provide technical guidance and conduct design reviews
Perks/Benefits
- N/A
Skills/Tech-stack
API deployment | Adversarial Testing | Auto Retraining | Azure ML | CI/CD | Container Orchestration | Data Preprocessing | Deep learning | Drift Detection | Evaluation | Experiment tracking | Fine Tuning | GDPR | Generative AI | Git | HIPAA | Language Models | Language Processing | Language Understanding | Large Language Models | LoRA | MLOps | Machine Learning | Model Monitoring | Model Serving | Natural Language | Natural Language Processing | Natural Language Understanding | PEFT | Prompt engineering | PyTorch | Python | RAG | Retrieval-Augmented Generation | Speech-to-Text | Synthetic data | Tacotron | TensorFlow | Text to Speech | Vector Databases | Whisper
Education
Roles
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