GenAI Architect

Job Description Must Have Technical/Functional Skills Programming: Python (primary), familiarity with TypeScript/Node.js for service integration Frameworks: LangChain, LlamaIndex, LangGraph, AutoGen (agent orchestration) Vector databases: Pinecone, Weaviate, Milvus, pgvector, Chroma LLM/API platforms: OpenAI, Anthropic Claude, Azure OpenAI, AWS Bedrock, Google Vertex AI, Hugging Face Fine-tuning techniques: LoRA, QLoRA, PEFT, RLHF fundamentals Infrastructure: Docker, Kubernetes, serverless deployment on Azure/AWS/GCP MLOps/LLMOps tooling: MLflow, Weights & Biases, evaluation/observability frameworks (e.g., LangSmith, Arize) Data engineering: ETL pipelines, embeddings generation, knowledge graph integration API design and microservices architecture for AI-powered applications Roles & Responsibilities Define enterprise Gen AI strategy, reference architecture, and technology standards Evaluate, select, and benchmark LLM providers and foundation models for specific use cases Architect RAG pipelines, agentic workflows, and multi-agent orchestration systems Establish responsible AI practices: guardrails, content filtering, bias and hallucination mitigation Lead Gen AI solutions from proof-of-concept through production-grade deployment Partner with data science, engineering, and business teams to prioritize and scope use cases Monitor model performance, cost, latency, and drift in production environments Mentor engineering teams on Gen AI best practices and emerging techniques Stay current with the rapidly evolving Gen AI landscape and advise leadership on adoption roadmap

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