- Collaborate directly with enterprise clients and internal stakeholders to deeply understand business goals, operational workflows, data landscapes, and technical constraints.

- Design, build, and deploy AI-driven solutions, integrating core AI platforms into customer systems and workflows.

- Translate complex business problems into viable AI solutions, focusing on where AI agents can augment or replace manual processes.

- Develop and maintain robust, scalable AI-powered systems, ensuring efficiency, reliability, and seamless integration with existing IT frameworks.

- Implement multi-agent systems and advanced AI solutions, utilizing techniques such as prompting, memory management, tool usage, and behaviour planning with Large Language Models (LLMs).

- Rapidly prototype and productionize solutions, working with client teams to whiteboard concepts, build prototypes, and iterate quickly based on feedback.

- Build connectors and data pipelines for secure and efficient data flow between client systems and AI platforms.

- Act as a technical solutions expert, providing troubleshooting, analysis, and support for AI-driven architectures.

- Provide training, documentation, and enablement to client business and operations teams, fostering self-sufficiency and adoption of AI tools.

- Serve as a critical feedback loop to internal product and engineering teams, influencing the roadmap based on customer insights and real-world deployment challenges.

Technical Skills :

- Strong Programming Language Proficiency : Expert-level skills in at least one modern programming language (e.g., Python, Java).

- Strong AI Literacy : Demonstrable understanding of AI/ML fundamentals, including concepts like LLMs, RAG (Retrieval Augmented Generation), embeddings, and vector databases.

- Hands-on experience with cloud platforms (AWS/GCP/Azure), including familiarity with MLOps tooling.

- Experience with API design and integration for AI services and enterprise systems (e.g., Salesforce, ServiceNow).

- Solid understanding of software development lifecycle (SDLC) best practices and agile methodologies.

Good To Have :

- Agentic AI Hands-on Experience : Practical experience in designing, building, and deploying AI agents or multi-agent systems that exhibit autonomous, proactive, and adaptive behaviours.

- Familiarity with containerization technologies (Docker, Kubernetes).

- Experience with data analytics and visualization tools.

- Prior experience in the commercial real estate or adjacent industries.