Lead Machine Learning Engineer
USD 171K-230K Senior-level Full Time
Tasks
- Build model training pipelines
- Communicate technical strategy
- Conduct AI research and experimentation
- Create evaluation frameworks
- Design AI/ML systems
- Detect hallucinations
- Develop agentic workflows
- Establish responsible AI frameworks
- Implement inference services
- Implement observability infrastructure
- Implement safety guardrails
- Implement testing and deployment
- Measure LLM quality and safety
- Monitor models in production
- Orchestrate multi agent platforms
- Own AI ML lifecycle
- Resolve technical blockers
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | AWS Kinesis | Agentic Systems | Apache Kafka | Azure | Azure Event | Azure Event Hubs | CI/CD | Cloud Native | Cloud platform | Cloud-Native ML | CrewAI | Deep learning | Embeddings | Event Hubs | Google Cloud | Google Cloud Platform | Google Pub/Sub | Hallucination detection | Human Feedback | Langchain | Language Models | Large Language Models | Learning from Human Feedback | LoRA | MLOps | Machine Learning | Model Distillation | Model Evaluation | Model Monitoring | Model versioning | Multimodal Models | NumPy | Observability | Pandas | Prompt engineering | Pub/Sub | PyTorch | Python | QLoRA | RAG | RLHF | Reinforcement Learning | Reinforcement Learning from Human Feedback | Responsible AI | Retrieval-Augmented Generation | Safety Guardrails | Scikit-learn | Streaming Data | Supervised Learning | TensorFlow | Unsupervised Learning | Vector Databases
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
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