Architect / Principal, AI ML Engineer
Tasks
- Align machine learning strategy with business goals
- Architect cloud-native AI solutions
- Build vector search using vector databases
- Define end to end Gen AI architecture
- Define end-to-end AI ML architecture
- Design RAG systems
- Design data pipelines
- Design model training and inference
- Ensure ethical and responsible AI practices
- Evaluate AI tools and platforms
- Evaluate build-vs-buy decisions
- Guide MLOps for CI CD and model monitoring
- Improve model interpretability
- Integrate generative AI into enterprise applications
- Lead MLOps implementation
- Mentor engineering teams
- Perform bias detection
- Perform model versioning and automated retraining
- Provide client solution design consulting
- Recommend AI adoption strategies
- Support AI governance and compliance
Perks/Benefits
- Career growth opportunities
- Flexible work schedule
- Generous time off
- Global team collaboration
- Mental Health plans
- Remote-first work environment
- Wellbeing programs
Skills/Tech-stack
APIs | AWS SageMaker | Agentic AI | Automated retraining | Azure Machine Learning | CI/CD | Data Governance | Deep learning | Docker | Embeddings | Faiss | Fine Tuning | Generative AI | Google Cloud | Google Cloud Vertex | Google Cloud Vertex AI | Kubeflow | Kubernetes | LLM APIs | MLOps | MLflow | Machine Learning | Microservices | Model Monitoring | Model Risk Management | Model risk | Model versioning | OpenAI API | Pinecone | Prompt engineering | PyTorch | Python | Retrieval-Augmented Generation | Risk Management | Sagemaker Pipelines | Scikit-learn | TensorFlow | Vector Databases | Vertex AI | Weaviate | XGBoost
Education
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