Senior AI Solution Architect

Role overview

Key responsibilities

• Design end-to-end AI/ML architectures and define AI strategy, architecture patterns, technology stacks, and roadmaps.
• Architect and implement AI, ML, Generative AI, and LLM solutions; design RAG architectures, vector database integrations, and retrieval pipelines.
• Evaluate AI models, frameworks, platforms, and cloud services; design scalable applications, APIs, microservices, and enterprise integrations.
• Implement cloud-based AI solutions and establish MLOps practices for deployment, monitoring, versioning, optimization, and lifecycle management.
• Define responsible AI practices for privacy, security, explainability, bias mitigation, and governance.
• Assess feasibility, scalability, cost, and business value; collaborate across technical teams and business stakeholders.
• Develop architecture diagrams, specifications, proofs of concept, and implementation plans; guide development teams and monitor emerging technologies.
• Ensure enterprise security, compliance, performance, availability, and reliability requirements are met.

Required qualifications

• 8–10 years of experience in AI, machine learning, data science, software engineering, or AI-focused solution architecture.
• Proven experience designing and delivering enterprise-scale AI/ML solutions.
• Strong understanding of machine learning, deep learning, NLP, and predictive analytics.
• Hands-on experience with Generative AI, LLMs, prompt engineering, and LLM application development.
• Experience with RAG pipelines, embeddings, vector databases, and semantic search.
• Proficiency in Python; familiarity with PyTorch, TensorFlow, Scikit-learn, or Hugging Face.
• Experience with LangChain, LlamaIndex, or equivalent; strong knowledge of REST APIs, microservices, distributed systems, and enterprise integration.
• Experience with at least one of AWS, Microsoft Azure, or Google Cloud.
• Understanding of data engineering, data pipelines, data lakes, data warehouses, data governance, MLOps, CI/CD, and production model monitoring.
• Strong understanding of AI security, data protection, responsible AI, and governance; strong architecture, analytical, problem-solving, communication, and stakeholder management skills.

Preferred qualifications

• Experience with Azure AI Foundry, Amazon Bedrock, Google Vertex AI, or equivalent enterprise AI platforms.
• Experience with AI agents, tool calling, multi-agent workflows, enterprise copilots, and vector databases such as Pinecone, Weaviate, Milvus, or pgvector.
• Experience with Docker, Kubernetes, model evaluation, hallucination mitigation, guardrails, AI observability, or hybrid and multi-cloud AI architectures.
• Relevant cloud architecture, AI/ML, or Generative AI certifications; experience leading technical teams and presenting to senior stakeholders.