Job Description

Key Responsibilities

• Define the GenAI architecture strategy , ensuring alignment with enterprise technology standards, security frameworks, and governance principles.
• Design and oversee the implementation of LLM-based applications , integrating large-scale models such as GPT, Claude, Gemini, or custom fine-tuned models into enterprise systems.
• Partner with business and technology stakeholders to identify high-value AI use cases across automation, natural language processing, knowledge management, and data enrichment.
• Evaluate and guide the adoption of cloud-native AI services (Azure OpenAI, AWS Bedrock, Vertex AI, etc.) and vector database technologies (Pinecone, Chroma, Milvus, etc.).
• Lead AI model orchestration , retrieval-augmented generation (RAG) pipelines, and multi-agent workflows to create scalable and context-aware solutions.
• Establish best practices for prompt engineering, model evaluation, performance monitoring, and ethical AI standards.
• Collaborate with data engineering and DevOps teams to ensure MLOps readiness , including CI/CD for model deployment and versioning.
• Mentor cross-functional teams on AI design patterns and help drive AI literacy and innovation culture across the organization.

Required Qualifications

• 10+ years of experience in software architecture, with at least 3+ years in AI/ML solution design or GenAI-focused implementations.
• Proven experience designing and deploying LLM-powered applications in enterprise environments.
• Strong knowledge of Python, REST APIs, vector search, embeddings, and orchestration frameworks such as LangChain, LlamaIndex, or Semantic Kernel.
• Experience with cloud AI ecosystems (Azure OpenAI, AWS Bedrock, GCP Vertex AI).
• Understanding of data governance, privacy, and security principles related to AI workloads.
• Excellent communication and stakeholder management skills, with the ability to translate complex AI concepts into actionable business value.

Preferred Skills

• Experience in retrieval-augmented generation (RAG) , multi-agent design, or fine-tuning transformer models.
• Background in enterprise data architecture , analytics platforms, or cognitive automation.
• Prior experience working in real estate, financial services, or large-scale enterprise transformation programs is a plus.

What You’ll Bring

• A passion for solving complex business challenges using Generative AI.
• Ability to bridge the gap between data science, engineering, and enterprise architecture.
• Leadership presence to influence executive and technical stakeholders alike.

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