Sde Ii (Ai/Ml)
Tasks
- Build RAG pipelines and retrieval systems
- Define and track model quality metrics
- Design end to end architecture for AI modules
- Develop evaluation frameworks for LLM output quality
- Implement prompt engineering and few shot design
- Improve AI ML reliability performance cost efficiency
- Lead low level design and document designs
- Maintain CI CD and deployment pipelines
- Mentor engineers and interns
- Monitor AI services in production
- Optimize LLM inference latency throughput cost
- Own production AI ML components
- Respond to production incidents and run root cause analysis
- Set up observability and instrumentation for agent workflows
Perks/Benefits
- Comprehensive benefits
- Continuous learning and professional development
- Equity options
- Flexible work arrangements
Skills/Tech-stack
AWS | CI/CD | Cause analysis | CrewAI | Datadog | Docker | Embeddings | Few-Shot Learning | Few-shot | GitHub Copilot | Grafana | Incident Response | Inference Optimization | Integration Testing | Kubernetes | LLM Evaluation | Langchain | Langfuse | Langgraph | Language Models | Large Language Models | Machine Learning | Model Inference | Model Inference Optimization | Observability | Prometheus | Prompt engineering | Python | RAG | Regression testing | Retrieval-Augmented Generation | Root Cause Analysis | Root cause | Unit Testing | Vector Databases | Weights and Biases
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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