Senior AI Solution Architect

Job Summary We are seeking an experienced AI Solution Architect with 8 10 years of experience in Artificial Intelligence (AI), Machine Learning (ML), and enterprise solution architecture. The ideal candidate will be responsible for designing, developing, and implementing scalable, secure, and high-performance AI solutions aligned with business objectives. The candidate should have strong expertise in AI/ML architecture, Generative AI, Large Language Models (LLMs), cloud-based AI platforms, and enterprise system integration. The role requires close collaboration with business stakeholders, data scientists, software engineers, and technology teams to translate business requirements into effective AI-driven solutions. Key Responsibilities • Design end-to-end AI/ML solution architectures aligned with business requirements and enterprise technology standards. • Define AI strategy, architecture patterns, technology stacks, and implementation roadmaps. • Architect and implement AI, ML, Generative AI, and LLM-based solutions for enterprise use cases. • Design Retrieval-Augmented Generation (RAG) architectures, vector database integrations, and knowledge retrieval pipelines. • Evaluate and select appropriate AI models, frameworks, platforms, and cloud services based on business and technical requirements. • Develop architecture for scalable AI applications, APIs, microservices, and enterprise integrations. • Design and implement AI solutions using cloud platforms such as AWS, Microsoft Azure, or Google Cloud. • Establish best practices for model deployment, monitoring, versioning, performance optimization, and lifecycle management using MLOps principles. • Define responsible AI practices, including data privacy, security, explainability, bias mitigation, and governance. • Assess the feasibility, scalability, cost, and business value of proposed AI solutions. • Collaborate with data scientists, ML engineers, data engineers, developers, and business stakeholders throughout the solution lifecycle. • Develop architecture diagrams, technical specifications, proof of concepts (POCs), and implementation plans. • Guide development teams on AI architecture, coding standards, integration patterns, and deployment practices. • Monitor emerging AI technologies and recommend innovations that improve business outcomes. • Ensure AI solutions meet enterprise security, compliance, performance, availability, and reliability requirements. Preferred Qualifications • Experience with enterprise AI platforms such as Azure AI Foundry, Amazon Bedrock, Google Vertex AI, or equivalent services. • Experience implementing AI agents, tool calling, multi-agent workflows, and enterprise copilots. • Familiarity with vector databases such as Pinecone, Weaviate, Milvus, or pgvector. • Experience with containerization and orchestration technologies such as Docker and Kubernetes. • Knowledge of model evaluation, hallucination mitigation, guardrails, and AI observability. • Experience designing hybrid or multi-cloud AI architectures. • Relevant certifications in cloud architecture, AI/ML, or Generative AI. • Experience leading technical teams and presenting architecture solutions to senior stakeholders. Key Competencies • AI/ML solution architecture and technology strategy. • Generative AI, LLMs, and RAG implementation. • Enterprise architecture and system integration. • Cloud-native AI solution design. • MLOps and production AI lifecycle management. • AI security, governance, and responsible AI. • Technical leadership and stakeholder collaboration. • Performance, scalability, and cost optimization. • 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, Natural Language Processing (NLP), and predictive analytics. • Hands-on experience with Generative AI, LLMs, prompt engineering, and LLM application development. • Experience designing RAG pipelines, embeddings, vector databases, and semantic search solutions. • Proficiency in Python and familiarity with AI/ML libraries such as PyTorch, TensorFlow, Scikit-learn, or Hugging Face. • Experience with AI frameworks and orchestration tools such as LangChain, LlamaIndex, or equivalent technologies. • Strong knowledge of REST APIs, microservices, distributed systems, and enterprise integration patterns. • Experience with at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud. • Understanding of data engineering concepts, data pipelines, data lakes, data warehouses, and data governance. • Knowledge of MLOps practices, CI/CD pipelines, model monitoring, and production deployment. • Strong understanding of AI security, data protection, responsible AI, and governance frameworks. • Excellent architectural design, analytical, problem-solving, communication, and stakeholder management skills.