Applied AI Engineer
Tasks
- Build AI agents with domain context
- Collaborate with data engineers developers and analysts
- Deploy and maintain generative AI solutions in production
- Design agentic workflows for production pipelines
- Document work and follow LLMOps best practices
- Evaluate and optimize agent performance
- Integrate agents with client applications and APIs
- Orchestrate LLMs retrieval systems and external tools
Perks/Benefits
- Conference support
- Dedicated training time
- Flexible schedule
- Modern equipment and tools
- Professional development budget
- Remote work
Skills/Tech-stack
APIs | AWS | Anthropic API | Autogen | Azure | CrewAI | Docker | Embedding pipelines | Function Calling | GCP | Google AI | Kubernetes | LLM orchestration | LLMOps | Langchain | Langfuse | Langgraph | Langsmith | Language Models | Large Language Models | Logging | Microservices | Observability | OpenAI API | PGVector | Pinecone | Prompt engineering | PyTorch | Python | Qdrant | RAG | Retrieval-Augmented Generation | Scikit-learn | Structured evaluation | TensorFlow | Tool use | Tracing | Vector Databases | Weaviate | Weights and Biases
Education
N/A
Roles
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