AI Architect

Overview:

The Generative AI Architect will lead the design, development, and deployment of enterprise-grade generative AI solutions leveraging LLMs, diffusion models, and multimodal architectures. This role combines deep technical expertise with solution architecture, governance, and strategic advisory skills to enable responsible and scalable adoption of GenAI across business domains.

You will work cross-functionally with data scientists, software engineers, risk and compliance teams, and business stakeholders to define reference architectures, integrate with existing systems, and ensure secure, ethical, and compliant AI adoption.

Responsibilities:

Architecture & Solution Design

• Design scalable and modular architectures for generative AI solutions (LLMs, chatbots, copilots, document intelligence, image/video generation, etc.).
• Define integration patterns between LLM platforms (e.g., OpenAI, Azure OpenAI, Anthropic, Google Gemini) and enterprise systems (e.g., SAP, ServiceNow, Salesforce).
• Build reference architectures, reusable components, and frameworks for GenAI use cases.
• Select and benchmark foundational models and fine-tuning strategies (RAG, LoRA, adapter-based tuning).

Technical Leadership

• Drive end-to-end solution development from ideation to deployment.
• Develop proof-of-concepts and pilots to demonstrate ROI and feasibility.
• Collaborate with data engineers to design data pipelines and vector stores for retrieval-augmented generation (RAG).
• Guide MLOps and AIOps teams on model lifecycle management, prompt orchestration, versioning, and observability.
• Lead the evaluation and adoption of GenAI platforms, APIs, and orchestration tools (Microsoft AutoGen, LangChain, Semantic Kernel, etc.).
• Define guardrails for prompt safety, hallucination mitigation, and explainability

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
• 10+ years of total experience, including 4+ years in AI/ML solution architecture or enterprise data systems.
• Proven experience designing or deploying solutions using LLMs.
• Strong programming and architecture skills in Python, API design, and microservices.
• Experience with Azure AI Studio, AWS Bedrock, or Google Vertex AI.
• Hands-on experience with vector databases and prompt engineering.
• Familiarity with DevOps/MLOps tools (Docker, Kubernetes, MLflow, Databricks, Azure ML, etc.).

Preferred Skills and Experience:

• Experience in building enterprise AI copilots or domain-specific LLM agents.
• Familiarity with frameworks like LangChain, LlamaIndex, Semantic Kernel, or Haystack.
• Strong stakeholder communication and the ability to translate technical complexity into business value.
• Experience with multimodal models (text, image, voice, video).
• Strong analytical and problem-solving mindset.
• Ability to operate in a highly cross-functional environment.
• Strategic thinking with hands-on execution ability.
• Excellent communication and presentation skills.