AI/ML Engineer
Tasks
- Build evaluation frameworks for regressions
- Build production AI systems
- Create training pipelines for production data
- Deploy LLM inference services with monitoring
- Design and implement FastAPI endpoints
- Ensure reliability with testing debugging profiling
- Fine-tune and evaluate models
- Handle streaming responses retries and rate limits
- Implement data pipelines for DSLM training
- Integrate ML capabilities into Python backend
- Mentor engineers through code review
- Optimize inference performance
- Use Docker for deployment
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Anthropic API | Axolotl | Batching | CI/CD | Caching | DeepSpeed | Distributed Training | Docker | FSDP | FastAPI | Fine Tuning | Graph Databases | Kubernetes | LLM | LoRA | Memgraph | Neo4j | OpenAI API | Postgres | Prompt engineering | PyTorch | PydanticAI | Python | QLoRA | Quantization | Rate Limiting | Retries | Streaming | Transformers | Unsloth | VLLM | Weights and Biases
Education
N/A
Roles
AI | AI/ML Engineer | Engineer | Learning Engineer | ML Engineer | Machine Learning Engineer
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