Data And AI Architect

Description We are looking for a Data & AI Architect with strong hands-on experience to design and deliver data-driven and AI solutions. This role focuses on building practical, scalable systems while working closely with clients to understand requirements and translate them into working solutions. The ideal candidate brings around 10+ years of overall experience, with recent focus on GenAI, LLMs, and multi-agent systems, and is comfortable operating across both architecture and implementation.

Requirements

Responsibilities

• Design and implement data and AI solutions, including GenAI and LLM-based systems
• Work directly with clients to understand business needs and translate them into technical solutions
• Lead solution design from concept to delivery, ensuring practical and scalable architecture
• Contribute hands-on to development when needed (prototyping, architecture reviews, troubleshooting)
• Build and deploy multi-agent and AI-driven workflows where relevant
• Collaborate with engineering, data, and business teams to ensure smooth delivery
• Support pre-sales discussions with RFP, solution ideas, demos, and technical inputs
• Ensure solutions follow good practices in scalability, security, and maintainability
  
  

• 10+ years of experience in data, software, or AI-related roles
• Strong hands-on experience with GenAI, LLMs, and multi-agent systems (at least 4 years)
• Proven track record of delivering end-to-end solutions from requirements to production
• Good understanding of data platforms, APIs, and system design
• Experience working in client-facing roles and managing expectations
• Ability to balance architecture thinking with practical implementation
• Strong communication skills for both technical and non-technical stakeholders Technical Exposure
• Experience with modern data platforms (data lakes, warehouses, streaming where needed)
• Familiarity with LLM ecosystems, orchestration frameworks, and AI deployment patterns
• Cloud experience (AWS, Azure, or GCP)
• Understanding of microservices, APIs, and event-driven architectures
• Exposure to MLOps / LLMOps practices
  
  

• Visa Sponsorship
• Health Insurance
• Development Plans