AI/ML & Forward Deployed Engineer
Tasks
- Build AI evaluation frameworks
- Build GenAI applications
- Containerize services with Docker
- Create scalable AI services
- Deploy AI solutions
- Detect model drift
- Develop machine learning models
- Ensure data governance
- Implement CI/CD pipelines
- Implement encryption
- Implement event-driven architectures
- Implement rag pipelines
- Implement role-based access control
- Integrate REST APIs
- Integrate gRPC APIs
- Monitor model performance
- Optimize AI systems
- Orchestrate services with Kubernetes
- Perform prompt engineering
- Set up LLMOps pipelines
- Set up MLOps pipelines
- Support production operations
- Troubleshoot model issues
Perks/Benefits
- Collaborative environment
- Cutting-edge projects
- Hands-on AI exposure
- Remote working model
- Stakeholder collaboration
Skills/Tech-stack
Anomaly Detection | Audit Logging | CI/CD | Classification | Data Governance | Data Quality | Deep learning | Docker | Drift Detection | Embeddings | Encryption | Event Driven | Event-driven architecture | Forecasting | GRPC | Kubernetes | LLM Evaluation | LLMOps | Language Models | Language Processing | Large Language Models | MLOps | Machine Learning | Model Drift | Model Drift Detection | Model Monitoring | Model versioning | NLP | Natural Language | Natural Language Processing | Prompt engineering | Python | RAG | RBAC | REST APIs | Regression | Reranking | Retrieval tuning | Retrieval-Augmented Generation | Security
Education
N/A
Roles
AI | AI Engineer | Engineer | LLMOps Engineer | Learning Engineer | MLOps Engineer | Machine Learning Engineer
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