Manage the complete sales cycle from lead generation, customer discovery, solution presentation, proposal preparation, negotiation, and contract closure.
Validate workflows and outputs, improve accuracy, reliability and efficiency based on user feedback, and maintain practical workflow documentation and user guides.
Cloud and DevOps Engineering: Implement and maintain CI/CD pipelines, automated testing, application monitoring and deployment processes. Troubleshoot production issues, optimise performance and improve application reliability.
Front-End & Back-End Integration: Participate in web application development, seamlessly integrating AI capabilities (API / LLM services) into business systems to deliver intuitive user experiences.
Validate workflows and outputs, improve accuracy, reliability and efficiency based on user feedback, and maintain practical workflow documentation and user guides.
Real, practical exposure to using GenAI / LLMs / coding assistants in delivery – beyond experimentation, with an understanding of enterprise governance.
Enhance and maintain user interfaces for usability and continuous improvement across various Channels including Voice/Telephony, Web Messaging, Mobile Applications, and WhatsApp
Production Engineering: Translate messy, real-world financial records (invoices, bank statements, contracts, ledgers) into structured data via OCR, NLP, and agentic workflows.
Champion rapid experimentation, prototyping and iterative development, using modern AI-assisted and vibe-coding approaches where appropriate to shorten development cycles
Establish adoption frameworks and ROI tracking mechanisms for deployed AI products (e.g., reduction in fraud losses, 30% content operations efficiency gain).
Design, establish, and maintain a scalable and secure AI architecture framework that supports deployment across edge, cloud, and on-premises environments.
Model Development & Training: Develop, train, and fine-tune foundation models for robotic manipulation, including VLA, VLM, action models, and multimodal models. Design and improve model architectures, training methods, and data pipelines.
Build and scale solutions, embedding new ways of working, driving adoption, managing risk, and ensuring AI is deployed responsibly and effectively in a way that improves both outcomes and operating models.