Applied AI ML Lead [Multiple Positions Available]
Tasks
- Build NLP pipelines
- Build retrieval-augmented generation
- Conduct code reviews
- Conduct system design reviews
- Define AI architecture roadmap
- Deploy machine learning models
- Design machine learning models
- Develop data preprocessing pipelines
- Develop generative AI solutions
- Document methodologies and logs
- Drive long term AI strategy
- Ensure reproducibility
- Evaluate AI models
- Fine tune custom LLMs
- Implement prompt engineering
- Integrate AI models for production
- Integrate LLMs into secure systems
- Lead AI engineering team
- Manage project timelines
- Mentor AI and ML engineers
- Monitor model performance
- Orchestrate workflow automation
- Perform escalation for LLM integration issues
- Prepare training data
- Translate business requirements into technical requirements
Perks/Benefits
- Backup childcare
- Financial coaching
- Health care coverage
- Mental health support
- On-site health and wellness centers
- Retirement savings plan
- Tuition reimbursement
Skills/Tech-stack
AWS | Accuracy | Agent systems | CI/CD | Cost Optimization | Deep learning | ECS | Embeddings | Ethical AI | Governance | Huggingface | Langchain | Language Models | Language Processing | Large Language Models | Low-Rank Adaptation | Model Evaluation | Model versioning | Multi-Agent | Multi-Agent Systems | Multimodal AI | Natural Language | Natural Language Processing | NumPy | OpenSearch | Pandas | Precisión | Prompt engineering | PySpark | PyTorch | Python | RESTful APIs | Recall | Reliability Engineering | Retrieval-Augmented Generation | SQL | Safety Guardrails | SageMaker | Scikit-learn | TensorFlow | Tokenization | Transformer Models | Vector Databases
Education
Bachelor of Science | Master of Engineering | Master of Science | PhD
Regions
Countries
States
Cities
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