Purpose of the role:

The Engineer - AI and Data is responsible for designing, developing, and deploying advanced AI-driven solutions that enhance operational efficiency, decision-making, and service delivery within facilities management and enterprise environments. The role spans predictive analytics, retrieval-augmented generation (RAG), and agentic AI systems, ensuring the delivery of scalable, reliable, and production-ready intelligence systems.

The role plays a critical part in building the organization’s AI capabilities by integrating machine learning models with large language models (LLMs), architecting intelligent automation frameworks, and developing reusable platforms for rapid deployment. The AI & Data Engineer ensures that solutions are grounded in data accuracy, optimized for performance, and aligned with business goals while maintaining high standards of reliability, governance, and safety.

The role includes but is not limited to:

Agentic AI Development:

• Design and develop multi-agent systems using planner-executor frameworks and tool-based orchestration across enterprise use cases.

• Architect agent memory systems include short-term context, long-term memory, and knowledge graph integrations.

• Define structured tool schemas and function-calling architectures ensuring reliable system outputs.

• Implement guardrails, fallback mechanisms, and human-in-the-loop checkpoints for critical workflows.

• Evaluate agent performance through task success rates, reasoning reliability, and failure pattern analysis.

RAG Architecture & Intelligence Systems:

• Design end-to-end RAG pipelines including data ingestion, chunking, embedding, retrieval, and generation layers.

• Implement advanced retrieval techniques such as multi-query retrieval, query routing, HyDE, and Graph RAG.

• Select and optimize vector databases (Pinecone, Weaviate, pgvector) based on latency and scalability requirements.

• Continuously improve retrieval performance by measuring precision, recall, and answer faithfulness.

• Integrate structured and unstructured data sources into unified knowledge retrieval systems.

Model Serving & Optimization:

• Manage model deployment and inference optimization using tools like vLLM and similar frameworks.

• Optimize model performance through batching, caching, quantization, and latency tuning.

• Lead prompt engineering strategies to enhance response quality and reliability.

• Implement fine-tuning techniques such as LoRA, PEFT, and SFT where necessary.

• Evaluate trade-offs between RAG, fine-tuning, and prompt engineering based on use case performance.

Evaluation, Safety & Governance:

• Build robust evaluation frameworks to measure quality, safety, latency, and cost of AI systems.

• Identify and mitigate risks such as hallucinations, prompt injections, and retrieval gaps.

• Develop and implement reusable evaluation mechanisms and benchmarking tools.

• Ensure compliance with AI governance, ethical guidelines, and enterprise policies.

• Monitor system performance post-deployment and implement continuous improvements.

Predictive Analytics & Machine Learning:

• Develop and deploy machine learning models for classification, regression, ranking, and forecasting tasks.

• Perform feature engineering, selection, and dimensionality reduction based on business context.

• Manage full ML lifecycle including experimentation, validation, deployment, and monitoring.

• Combine predictive models with LLM-based systems for intelligent decision-making workflows.

• Communicate analytical results clearly, including confidence levels and model explainability.

Collaboration & Engineering Excellence:

• Collaborate with product, engineering, and operations teams to design AI-driven solutions.

• Translate business problems into scalable technical implementations and production-ready systems.

• Contribute to internal AI frameworks, reusable modules, and engineering best practices.

• Support continuous learning and innovation within the AI and data engineering space.

• Drive iterative improvements based on real-world system usage and feedback.

Key Result Areas

Qualification / Experience

Stakeholder Engagement & Job Context

Functional Competencies