Senior Applied AI Engineer
Tasks
- Build ML pipelines and agentic AI solutions
- Collaborate with product and engineering teams to improve AI platform
- Create reusable deployment assets and evaluation harnesses
- Create safety guardrails and human review workflows
- Define AI quality metrics and evaluation frameworks
- Deploy AI solutions and manage production monitoring
- Develop and maintain golden datasets
- Lead end to end enterprise AI program delivery
- Mentor and review engineering designs and code
- Perform failure mode analysis and targeted improvements
- Run model and agent evaluation loops
- Scope and design AI solution architecture
- Serve as technical advisor to customer stakeholders
- Translate business objectives into scalable ML solutions
Perks/Benefits
Skills/Tech-stack
A/B | A/B Testing | AWS | Ablation Testing | Agentic Workflows | Artificial Intelligence | Azure | B testing | Cloud platform | Error Analysis | Evaluation | Failure mode analysis | Google Cloud | Google Cloud Platform | Human-in-the-loop | LLM evals | Language Models | Large Language Models | MLOps | Machine Learning | Model Deployment | Monitoring | NumPy | Observability | Offline evaluation | Pandas | Prompt engineering | Python | RAG | Retrieval-Augmented Generation | Safety engineering | The Loop
Education
N/A
Regions
Countries
States
Cities
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