ML / AI Engineer
Tasks
- Apply spec-driven development
- Automate testing
- Build retrieval pipelines
- Build shared AI capabilities
- Collaborate with AI PM PO stakeholders
- Create orchestration layers
- Create technical artifacts specs evals interface definitions workflow contracts
- Define evaluation driven development loops
- Design production-grade AI systems
- Develop agentic workflows
- Ensure AI reliability observability governance security
- Implement AI-assisted development workflows
- Implement LLM experiences
- Measure AI quality reliability latency cost grounding
- Partner with product teams integration support
- Prototype and validate new AI approaches
- Support peer review and knowledge sharing
Perks/Benefits
Skills/Tech-stack
API | AWS | Agentic Workflows | Automated testing | Azure | Cloud infrastructure | Data Pipelines | Evaluation-Driven Development | Governance | LLMs | Observability | Orchestration | Python | Responsible AI | Retrieval-Augmented Generation | Security | Spec Driven | Spec Driven Development | Tool use
Education
Roles
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