- Company Name
- Simpliigence
- Job Title
- GenAI Architect
- Job Description
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**Job Title:** GenAI Architect
**Role Summary:**
Design, govern, and scale enterprise‑grade Generative AI (GenAI) solutions. Own the end‑to‑end architecture, model selection, integration, and operationalization to deliver measurable business value while ensuring security, compliance, and responsible AI practices.
**Expectations:**
- Define complete GenAI solution architectures, including model choice, orchestration, data pipelines, and integration layers.
- Create scalable patterns for Retrieval‑Augmented Generation, autonomous agents, fine‑tuning, and prompt engineering.
- Develop reference architectures, reusable components, and best‑practice guidelines for GenAI adoption.
- Evaluate and select LLMs and GenAI platforms (open‑source and commercial) based on cost, performance, security, and regulatory fit.
- Architect hybrid solutions that mix cloud‑hosted and on‑prem GenAI models when required.
- Provide architectural oversight for integration with APIs, microservices, workflows, and enterprise data platforms.
- Define MLOps/LLMOps standards for model versioning, monitoring, observability, and lifecycle management.
- Ensure compliance with data privacy, security, and Responsible AI principles.
- Collaborate with legal, security, and compliance to build governance frameworks.
- Partner with business leaders to identify high‑impact GenAI use cases, translate technical concepts into business outcomes, and mentor teams on GenAI concepts.
**Key Responsibilities:**
1. Lead design of end‑to‑end GenAI architectures and document patterns.
2. Build scalable RAG, agent, fine‑tuning, and prompt‑engineering workflows.
3. Create reference architecture artifacts and reusable component libraries.
4. Conduct LLM and platform evaluations (OpenAI, Azure OpenAI, Anthropic, LLaMA, Mistral, etc.).
5. Author hybrid architecture blueprints combining cloud and on‑prem deployments.
6. Partner with engineering to embed GenAI models into APIs, microservices, and enterprise systems.
7. Integrate vector databases (Pinecone, FAISS, Azure AI Search) and data platforms.
8. Establish MLOps/LLMOps pipelines (model versioning, monitoring, observability).
9. Enforce security, data privacy, and Responsible AI compliance across the stack.
10. Conduct stakeholder workshops to surface GenAI opportunities and advise on business impact.
11. Mentor cross‑functional teams on GenAI architecture, tooling, and best practices.
**Required Skills:**
- Deep expertise in LLMs, including OpenAI, Anthropic, LLaMA, Mistral, and fine‑tuning methods.
- Architecture experience with Retrieval‑Augmented Generation, autonomous agents, and prompt engineering.
- Cloud platform fluency (Microsoft Azure, Azure OpenAI, Kubernetes, Azure AI Search).
- Proficiency with vector databases and retrieval (Pinecone, FAISS, Azure AI Search).
- Familiarity with GenAI frameworks (LangChain, LlamaIndex, Semantic Kernel).
- Strong Python, REST API, and microservices integration skills.
- MLOps/LLMOps expertise (MLflow or equivalent) and model lifecycle management.
- Knowledge of Responsible AI principles, data privacy, and security compliance.
- Excellent stakeholder communication and advisory capabilities.
**Required Education & Certifications:**
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or related field.
- 10+ years of overall IT experience, with 3–5+ years focused on AI/ML and Generative AI architecture.