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.