Tasks:
- * Architect cloud-native AI solutions
- * Build evaluation harnesses and human-in-the-loop workflows
- * Build reusable AI tools and accelerators
- * Build scalable LLMOps pipelines
- * Define reference architectures for knowledge pipelines and LLM integrations
- * Design and build agentic systems and memory systems
- * Develop APIs and data pipelines
- * Document practices and mentor engineers
- * Establish AI engineering standards
- * Implement red-teaming and bias testing
- * Improve testing and prompt evaluation
- * Integrate AI with enterprise platforms
- * Lead AI technical delivery
- * Optimize model selection and costs
- * Ship LLM-powered features to production
Perks/Benefits:
- + Annual offsite
- + Hybrid working
- + Internal learning communities
- + Internal performance coaching
- + Monthly team lunches
- + Unlimited LMS access
Skills/Tech stack required:
[A/B] [A/B evaluation] [AI Governance] [Amazon SageMaker] [APIs] [AWS] [AWS Bedrock] [AWS Lambda] [AWS Step Functions] [Azure] [Bias Testing] [Cost monitoring] [CrewAI] [Distributed Systems] [Drift Detection] [Enterprise Integration] [Enterprise Integration Patterns] [ETL/ELT] [GCP] [Integration Patterns] [Java] [Langchain] [Langgraph] [Llamaindex] [LLMOps] [Microservices] [Prompt versioning] [Python] [Red Teaming] [SQL] [Step Functions] [TypeScript] [Unstructured Data] [Vector Databases]
Educational requirements:
N/A
Role(s):
[AI Engineer] [Engineer] [Senior AI Engineer]