Senior AI Engineer
Tasks
- Build data ingestion pipelines
- Build end-to-end AI systems
- Construct prompts
- Debug AI production issues
- Deploy production AI systems
- Design AI solution
- Develop API based AI services
- Evaluate model and workflow trade offs
- Implement configurable policies and kill switches
- Implement error handling and fallbacks
- Implement guardrails and safety controls
- Implement input validation
- Implement logging, metrics, and tracing
- Integrate ML and LLM orchestration
- Manage AI workload cost
- Mitigate prompt injection
- Perform feature engineering
- Plan scalability and reliability
- Refactor prototype to production
- Select traditional ML or LLM approach
- Translate business problems to AI systems
- Tune performance and optimize latency
Perks/Benefits
Skills/Tech-stack
AWS | Agent Orchestration | Agent systems | Azure | CI/CD | Cost Optimization | Distributed Systems | Docker | Failover | GCP | GRPC | Generative AI | Input validation | Java | Kubernetes | Langchain | Langgraph | Language Models | Large Language Models | Latency optimization | Logging | Machine Learning | Model Evaluation | Monitoring | Multi-Agent | Multi-Agent Systems | Node.js | Performance Tuning | Prompt engineering | PyTorch | Python | REST | Rate Limiting | Reliability | Retrieval-Augmented Generation | Scalability | Scikit-learn | TensorFlow | Tracing
Education
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