Senior AI/ML Engineer
Tasks
- Build LLM pipelines
- Create evaluation frameworks
- Define success metrics and deliver solutions
- Deploy and monitor AI models
- Design scalable ML systems
- Develop RAG systems
- Drive CI/CD and automation
- Ensure responsible AI governance fairness and transparency
- Establish feedback loops and datasets
- Implement model versioning
- Implement prompt design and fine tuning
- Lead cross-functional AI initiatives
- Manage data pipelines and training
- Own ML lifecycle end-to-end
- Scope and lead AI ML initiatives
- Set up observability and reproducibility
- Translate ML concepts for technical and non technical audiences
- Translate prototypes to production systems
Perks/Benefits
Skills/Tech-stack
AWS | Azure | Azure Machine Learning | CI/CD | Code review | Data Pipelines | Databricks | Direct Preference Optimization | Embeddings | Evaluation | Experiment tracking | Fairness | Fine Tuning | Governance | Huggingface | LLM Alignment | MLOps | Machine Learning | Model versioning | Modularity | Monitoring | Observability | Preference optimization | Prompt engineering | PyTorch | Python | Reinforcement Learning | Reproducibility | Retrieval-Augmented Generation | SQL | SageMaker | Snowflake | Supervised Fine Tuning | Supervised Learning | Testing | Transparency | Unsupervised Learning | Vector Databases | Vector Search
Education
N/A
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