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.).