AI Architect - RAG/LLM

Job Purpose :

We are seeking a highly experienced AI Architect with 10+ years in data and AI engineering, and deep hands-on expertise in Agentic AI systems, multi-LLM architecture, GenAI, Retrieval-Augmented Generation (RAG), and AI platform governance.

This role will lead the design and implementation of enterprise-grade AI solutions built on the Microsoft Azure ecosystem, combining architectural leadership with active engineering involvement.

The ideal candidate must be able to operate at both :

- Strategic architecture level

- Hands-on implementation level

Job Description / Duties and Responsibilities :

- Define AI/ML solution architecture and oversee implementation across projects using Microsoft Azure services.

- Lead and mentor a team of AI engineers and data scientists.

- Own the end-to-end lifecycle of AI models from ideation and data acquisition to deployment and monitoring.

- Partner with business stakeholders to translate requirements into AI solutions.

- Evaluate emerging tools, frameworks, and platforms for AI/ML development.

- Drive standardization, code quality, and best practices across the AI engineering team.

- Ensure ethical, explainable, and compliant AI practices.

- Define LLM routing, fallback, and evaluation strategies

- Design and implement autonomous AI agents using Azure OpenAI Service and Azure AI Studio

- Build multi-agent orchestration systems

- Implement memory management (short-term & vector-based long-term memory)

- Design agent safety guardrails and oversight mechanisms

- Architect end-to-end RAG pipelines

- Implement hybrid search (semantic + keyword)

- Optimize retrieval grounding and hallucination mitigation

- Tune prompts and evaluate model performance

Job Specification / Skills and Competencies :

- Bachelors or Masters degree in Computer Science, Artificial Intelligence, or a related field.

- 10+ years of experience in AI/ML development with demonstrated leadership in delivering production-grade AI solutions.

- Deep expertise in machine learning, deep learning, NLP, or computer vision.

- Experience with model architecture design, feature stores, model monitoring.

- Strong experience with MLOps, DevOps for ML, and deployment tools (Docker, Kubernetes, MLflow, Airflow).

- Solid understanding of cloud AI/ML offerings (Azure ML, SageMaker, Vertex AI).

- Proven ability to lead technical teams and manage delivery timelines.