Lead AI Engineer
Tasks
- Architect AI-powered features
- Build RAG-architectures
- Create documentation and runbooks
- Define AI governance and quality gates
- Design model training pipelines
- Develop LLM integrations
- Ensure access to unified high quality data
- Establish AI/ML standards and best practices
- Evaluate emerging AI technologies with proofs of concept
- Implement AI testing and safety measures
- Implement experiment tracking
- Lead AI research and development
- Lead AI technical roadmap
- Make build versus buy AI decisions
- Manage GPU resources
- Manage model versioning
- Mentor engineers in AI ML techniques
- Optimize inference performance
- Orchestrate multi-agent workflows
- Partner with engineering and architecture to deliver AI solutions
- Run experimentation frameworks for A B testing
- Serve ML models at scale
- Train evaluate and deploy custom ML models
- Translate business requirements into AI solutions
Perks/Benefits
- Access to training academy
- Flexible work model
- Mental physical financial wellbeing support
- Mentorship
- Professional learning program
- Remote work options
Skills/Tech-stack
A/B | A/B Testing | APIs | AWS | Agentic Architectures | Azure | B testing | Cloud Computing | Data Pipelines | Data Quality | Deep learning | Distributed Training | Embeddings | Experiment tracking | Feature Stores | Fine Tuning | GPU Optimization | GPU resource management | Google Cloud | Inference Optimization | JAX | Language Models | Large Language Models | MLOps | Machine Learning | Model Monitoring | Model Serving | Model versioning | Prompt engineering | PyTorch | RAG | Resource Management | Semantic Search | Shadow Deployments | System design | TensorFlow | Transformers | Vector Databases
Education
N/A
Roles
AI | AI Engineer | Engineer | Learning Engineer | Machine Learning Engineer
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