AI Engineer - RAG Pipelines

About the role :

You will build and own scoped AI service features within the Mind.AI platform. You work within the architecture set by the Lead AI Engineer, take feature specifications and deliver production-quality implementations: a chunking strategy module, a guardrail model integration, an embedding pipeline stage, a RAGAS metric computation job. You are expected to work independently within scope - take ownership, write tests, benchmark your work, and ship to the Lead's quality bar. You have built ML or AI features in production before. You know that a model that scores well in a notebook evaluation is not done - it needs to be packaged, served, monitored, and maintained. You are comfortable with the full lifecycle from experiment to production deployment.

Role & responsibilities :RAG & Knowledge Retrieval :

• Build and optimise enterprise-grade RAG pipelines for accurate knowledge retrieval

• Develop document ingestion, indexing, embedding, and retrieval workflows

• Implement hybrid search, re-ranking, and citation-based response generation

• Improve retrieval quality, relevance, and scalability across large knowledge bases

AI Safety & Guardrails :

• Implement PII detection, data protection, and content redaction controls

• Integrate prompt injection, toxicity, and misuse detection mechanisms

• Build AI guardrails to ensure safe, compliant, and trustworthy responses

• Develop automated response quality and faithfulness evaluation frameworks

Memory & Knowledge Management :

• Design and implement long-term AI memory frameworks

• Build user, agent, and organisational knowledge retention capabilities

• Develop knowledge graph and graph-based retrieval solutions

• Optimise context management through intelligent summarisation and memory retrieval

Evaluation & Optimisation :

• Define and implement AI evaluation metrics and testing frameworks

• Create and maintain golden datasets for model validation

• Conduct experiments to improve retrieval, reasoning, and response quality

• Drive continuous performance optimisation through benchmarking and analytics

Platform Engineering :

• Design scalable, production-ready AI services and APIs

• Optimise latency, throughput, reliability, and cost of AI workloads

• Build monitoring, observability, and auditability for AI systems

• Collaborate with platform, data, and product teams to deliver enterprise AI solutions