Senior AI Solution Architect

Role Overview

Senior AI Solution Architect at TAWANTECH. The ideal candidate will be responsible for designing, developing, and implementing scalable, secure, and high-performance AI solutions aligned with business objectives.

Role Purpose

Design end-to-end AI and ML solution architectures that translate business requirements into effective AI-driven solutions. Lead enterprise-scale AI implementations spanning strategy, architecture patterns, cloud platforms, and responsible AI governance, while collaborating closely with business stakeholders, data scientists, software engineers, and technology teams.

Key Responsibilities

Architecture Design & Strategy

• 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.
• Develop architecture for scalable AI applications, APIs, microservices, and enterprise integrations.
• Assess the feasibility, scalability, cost, and business value of proposed AI solutions.
• Develop architecture diagrams, technical specifications, proof of concepts (POCs), and implementation plans.

Technology Selection & Implementation

• Evaluate and select appropriate AI models, frameworks, platforms, and cloud services based on business and technical requirements.
• 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.
• Ensure AI solutions meet enterprise security, compliance, performance, availability, and reliability requirements.

Governance & Responsible AI

• Define responsible AI practices, including data privacy, security, explainability, bias mitigation, and governance.

Stakeholder Management & Leadership

• Collaborate with data scientists, ML engineers, data engineers, developers, and business stakeholders throughout the solution lifecycle.
• Guide development teams on AI architecture, coding standards, integration patterns, and deployment practices.
• Monitor emerging AI technologies and recommend innovations that improve business outcomes.

Qualifications & Experience

• 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.
• 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.

Skills & Competencies

Technical Skills

• 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.
• Experience with containerization and orchestration technologies such as Docker and Kubernetes.
• 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.
• Performance, scalability, and cost optimization.

Soft Skills

• Excellent architectural design, analytical, problem-solving, communication, and stakeholder management skills.
• Technical leadership and stakeholder collaboration.

Additional Information

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.
• 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.