Gen AI Solution Architect (Remote)

Senior AI/ML Engineer

• Design and architect end-to-end Generative AI solutions (text, image, speech, code, etc.) that meet business objectives and technical requirements.
• Define scalable architectures for LLM integration, prompt orchestration, retrieval-augmented generation (RAG), and multi-agent workflows.
• Evaluate and select appropriate AI models, frameworks, and cloud services (e.g., OpenAI, Anthropic, Hugging Face, Azure OpenAI, Vertex AI).
• Develop architecture blueprints, data flows, and API integration strategies.
• Provide technical direction and mentorship to development teams on GenAI solution design and deployment best practices.
• Partner with Data Scientists, ML Engineers, and MLOps teams to operationalize and monitor generative AI models.
• Guide the implementation of responsible AI practices, including bias detection, data governance, and model explainability.
• Stay ahead of emerging GenAI technologies, frameworks, and model capabilities.
• Prototype and evaluate new approaches (e.g., RAG, fine-tuning, LoRA, function calling, multi-agent systems) to accelerate solution development.
• Drive proof-of-concepts (PoCs) and pilot initiatives to demonstrate business value.
• Collaborate with business leaders, product managers, and clients to identify use cases, gather requirements, and define success metrics.
• Translate business challenges into AI-powered solutions with measurable outcomes.
• Present architecture designs and recommendations to technical and executive audiences.
  

Required Skills & Experience

• 8+ years of experience in software architecture, solution design, or AI/ML systems development.
• 2+ years specifically in Generative AI, LLM-based solutions, or AI product architecture.
• Strong hands-on experience with:
• OpenAI API, LangChain, LlamaIndex, Hugging Face, or similar frameworks
• Vector databases (e.g., Pinecone, Weaviate, FAISS, Milvus)
• Cloud AI platforms: Azure AI, AWS Bedrock, Google Vertex AI
• Python, FastAPI, RESTful APIs, microservices, and MLOps pipelines
• Deep understanding of LLM lifecycle management, prompt engineering, model evaluation, and data pipelines.
• Familiarity with enterprise AI architecture patterns security, scalability, monitoring, and governance.
• Strong presentation and stakeholder engagement skills.