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Gen AI Engineer
Role Overview
- This role sits at the intersection of traditional software engineering and machine learning.
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The primary goal is to solve complex enterprise problems by securely integrating AI endpoints into existing systems.
- Agentic AI & RAG: Design multi-step workflows, autonomous AI agents, and retrieval pipelines connecting models to proprietary, real-time data.
- Model Fine-Tuning & Prompt Engineering: Optimize existing foundational models using Parameter-Efficient Fine-Tuning (PEFT) like LoRA/QLoRA and systematically refine system prompts.
- Evaluation & Guardrails: Implement robust validation, golden dataset evaluations, and output guardrails to manage hallucinations and ensure safe, compliant responses.
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Deployment: Containerize models and integrate them as REST APIs into production environments.
- Programming Languages: Advanced proficiency in Python.
- Orchestration Frameworks: LangChain, LangGraph, LlamaIndex, or Hugging Face.
- Vector Databases: Pinecone, ChromaDB, Weaviate, or pgvector.
- Cloud AI Platforms: Amazon Web Services (AWS Bedrock), Microsoft Azure (Azure OpenAI), or Google Cloud (Vertex AI).
- Concepts: Transformer architectures, tokenization, model context protocol (MCP), and MLOps.
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