AI Engineer
Tasks
- Build knowledge base chunking and fine tuning
- Build multi-agent workflows
- Configure agents and prompts with selected LLM
- Contribute to CI/CD pipelines
- Define RAG success criteria and KPIs
- Deploy agent and knowledge base in production
- Design AI agents and automation solutions
- Design and operate RAG pipelines
- Design end to end agent flows
- Develop RPA tasks and enterprise integrations
- Document pipelines and agent configurations
- Implement root cause analysis and error handling
- Integrate RPA steps and external API calls
- Integrate upstream bots and APIs with RPA agents
- Monitor agents during hypercare
- Monitor and improve retrieval quality
- Write unit and integration tests
Perks/Benefits
- N/A
Skills/Tech-stack
API Integration | Agent memory | Anthropic API | Autogen | Automation Anywhere | CI/CD | CrewAI | Data Pipelines | Document chunking | Document processing | Driven systems | Embeddings | Event Driven | Event Driven Systems | Gemini API | Google Gemini | Google Gemini API | Guardrails | Integration Testing | LLM APIs | Langchain | Langgraph | LoRA | Microsoft Power | Microsoft Power Automate | Multi-step reasoning | OpenAI API | Power Automate | Prompt engineering | Python | QnA QnA | RAG | RPA | Retrieval evaluation | Structured prompting | Tool usage | UiPath | Unit Testing | Vector Databases | Vector Store
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
Roles
AI | AI Engineer | Developer | Engineer | Full Stack | Full Stack Developer
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