AI Architect

• Own the end-to-end AI solution architecture, ensuring:
  • Scalability
  • Security
  • Governance
  • Integration with enterprise systems
  • Alignment with business outcomes
• Define architecture standards and technical strategies for enterprise AI and Generative AI initiatives.
• Drive the design and implementation of scalable, secure, and production-ready AI solutions.
• Ensure AI platforms and solutions align with enterprise architecture, security, compliance, and business requirements.

• Define AI and GenAI solution architectures for prioritized business use cases.
• Design scalable, Azure cloud-native AI platforms and reusable reference architectures.
• Evaluate and select appropriate AI technologies, models, frameworks, and deployment patterns.
• Work with LLMs such as GPT-4/GPT-5 and other enterprise AI models based on solution requirements.
• Establish AI governance, security, compliance, responsible AI practices, and industry-standard AI guidelines.
• Design integration patterns with enterprise applications, APIs, data platforms, and Azure ecosystems.
• Drive architecture reviews, technical assessments, and architecture decision-making.
• Define non-functional requirements covering performance, scalability, cost optimization, resilience, availability, security, and observability.
• Collaborate with business stakeholders, product owners, engineering teams, and delivery teams to translate business requirements into technical solution blueprints.
• Guide MLOps and LLMOps implementation strategies, deployment models, and operational practices.
• Ensure required Azure infrastructure and environments are available for development teams to host applications, AI solutions, and dashboards.
• Review development team scripts, code, and technical implementations to ensure quality, security, scalability, and engineering standards.
• Establish coding, architecture, deployment, and AI development best practices.
• Mentor engineering and development teams on AI architecture, GenAI technologies, and solution implementation.
• Support technical teams in resolving architecture and integration challenges.
• Identify opportunities to optimize AI solutions for performance, reliability, scalability, and cost.
• Ensure AI solutions are designed for production readiness, monitoring, observability, and continuous improvement.
• Evaluate emerging AI technologies and recommend their adoption where they provide business or technical value.

Required Experience

• 8–10 years of overall experience in software development, AI engineering, solution architecture, or related technical roles.
• Hands-on development experience working on AI/ML or Generative AI projects.
• Proven track record of designing, developing, or architecting enterprise AI solutions.
• Strong experience translating business requirements into scalable AI solution architectures.
• Experience working with cloud-based AI platforms, preferably Microsoft Azure.
• Experience collaborating with engineering, product, business, security, and infrastructure teams.
• Strong understanding of enterprise architecture, integration patterns, and cloud-native solution design

• Strong understanding of Large Language Models (LLMs), including:
  • GPT
  • Llama
  • Claude
  • Gemini
• Hands-on knowledge of Retrieval-Augmented Generation (RAG) architectures.
• Experience with Agentic AI frameworks and agent-based solution design.
• Strong knowledge of prompt engineering techniques and best practices.
• Experience with AI model evaluation, performance assessment, and tuning.
• Understanding of AI/GenAI architecture patterns and deployment strategies.
• Knowledge of Azure cloud services and cloud-native AI architecture.
• Understanding of AI security, governance, compliance, and responsible AI principles.
• Strong understanding of APIs, enterprise integrations, data platforms, and distributed systems.
• Experience with MLOps/LLMOps concepts and production AI lifecycle management.
• Strong technical leadership, communication, problem-solving, and mentoring skills.

Preferred Tools & Certifications

• Azure Solutions Architect Expert – Mandatory
  • Strong knowledge of Azure services, architecture patterns, cloud infrastructure, and enterprise solution design.
• AI Governance-related certifications – Preferred
  • Knowledge of AI frameworks, responsible AI practices, model governance, risk management, and compliance.
• AI governance certification is preferred but not mandatory.

Education

• B.Tech or equivalent technical degree preferred.
• Relevant hands-on AI development and architecture experience may be considered alongside educational qualifications.