AI Architect

Role Overview

We are looking for highly skilled AI professionals who have designed and built complex AI solutions end-to-end , rather than only using pre-built AI libraries. The role involves architecting and developing scalable AI capabilities for Fireflink's cloud-based SaaS products, with strong ownership across design, implementation, optimisation, and deployment.

Depending on experience, the role will be positioned as AI Tech Lead / Associate AI Architect or AI Architect .

Key Responsibilities

• Design and develop AI-driven capabilities for cloud and automation SaaS products using NLP, Computer Vision, and Generative AI.
• Architect and implement complex AI solutions, including RAG-based systems , agentic workflows, and enterprise-grade GenAI use cases.
• Build and optimise NLP pipelines, document understanding, and vision-based automation using OpenCV and related techniques.
• Design scalable architectures using LangChain, LangGraph , and vector-based retrieval frameworks.
• Lead solution design discussions, perform technical reviews, and guide teams on best practices in AI engineering.
• Work closely with product, platform, and cloud teams to integrate AI models into production-grade systems.
• Drive innovation by evaluating emerging AI techniques, frameworks, and architectures relevant to SaaS platforms.
• Ensure robustness, scalability, security, and performance of AI solutions deployed in cloud environments.
• Mentor engineers and contribute to building strong AI engineering capabilities within the organisation (for Lead/Architect roles).

Required Skills & Experience

Primary Skills
• Strong hands-on experience in NLP , Computer Vision (OpenCV) , and Generative AI models .
• Experience designing and implementing RAG frameworks , agentic workflows, and context-aware AI systems.
• Hands-on expertise with LangChain and LangGraph for building complex AI pipelines.

Secondary Skills
• Strong programming experience with Python .
• Experience with AI/ML frameworks such as TensorFlow, PyTorch, Scikit-learn , or similar.
• Experience working with Vector Databases for semantic search and retrieval use cases.

Platform & MLOps
• Experience with MLOps , including model versioning, deployment, monitoring, and lifecycle management.
• Hands-on experience deploying AI models in cloud-based environments (Azure/AWS/GCP).
• Experience integrating AI models into scalable, production-grade SaaS platforms.

What We Are Specifically Looking For

• Professionals who have built complex AI solutions from the ground up , not just consumed AI libraries or APIs.
• Experience working on enterprise-scale, real-world AI projects involving architecture, trade-offs, and optimisation.
• Strong problem-solving mindset with the ability to translate business problems into robust AI architectures.