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.
(ref:hirist.tech)
