Principal AI/ML Engineer
Tasks
- Architect multi agent orchestration platform
- Benchmark model quality
- Build AI observability stack
- Build voice AI pipelines
- Create data ingestion and feature pipelines
- Define AI architectural standards and ADRs
- Deploy and monitor production AI systems
- Design MCP server architecture
- Drive ML CI CD with eval gating and canary releases
- Fine-tune and evaluate LLMs
- Implement hallucination detection and safety guardrails
- Implement rag pipelines
- Manage model lifecycle
- Mentor ML engineers and applied scientists
- Translate business problems into ML problem formulations
Perks/Benefits
- N/A
Skills/Tech-stack
ASR | Agent coordination | CI/CD | Context Management | DPO | Docker | Experiment tracking | Hallucination detection | Huggingface | Hybrid search | Keyword search | Kubernetes | LLM Inference | LLM Observability | Langfuse | Latency optimization | LoRA | MLOps | MLflow | Model Registry | Multi-Agent | Multi-Agent Coordination | PEFT | PII Redaction | PyTorch | Python | QLoRA | Quantization | RAG | RLHF | Rate Limiting | Re-ranking | Retrieval Re-ranking | Speech AI | TGI | TTS | TensorRT-LLM | Tool orchestration | Transformers | VAD | VLLM | Vector Search | Weights and Biases
Education
N/A
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