Architect / Principal, AI ML Engineer
Tasks
- Architect cloud native machine learning solutions
- Build retrieval augmented generation systems using vector databases
- Collaborate with leadership to align ML strategy with business goals
- Define end-to-end AI ML architecture
- Design Gen AI architecture and agentic AI systems
- Design data pipelines for model training and inference
- Develop MLOps pipelines with CI CD model monitoring and retraining
- Drive cross functional collaboration for ML opportunities
- Ensure ethical responsible AI practices
- Evaluate build versus buy and benchmark AI tools
- Integrate large language models into enterprise applications using APIs
- Mentor technical teams and guide AI solution design
Perks/Benefits
- Career development opportunities
- Flexible work environment
- Generous time off
- Mental Health plans
- Remote-first work environment
- Wellbeing programs
Skills/Tech-stack
Agentic AI | Amazon SageMaker | Automated retraining | Azure Machine Learning | CI/CD | Compliance | Data Governance | Deep learning | Docker | Embeddings | Faiss | Generative AI | Google Gemini | Google Vertex | Google Vertex AI | Kubeflow | Kubernetes | MLOps | MLflow | Machine Learning | Microservices | Model Inference | Model Monitoring | Model Risk Management | Model Training | Model risk | Model versioning | OpenAI API | Pinecone | Prompt engineering | PyTorch | Python | Retrieval-Augmented Generation | Risk Management | Scikit-learn | TensorFlow | Vector Databases | Vertex AI | Weaviate | XGBoost
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
Roles
AI | AI/ML Architect | AI/ML Engineer | Architect | Engineer | ML Architect | ML Engineer | Principal | Principal AI/ML Engineer
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