DUTIES AND RESPONSIBILITIES:

• Define and govern reference architectures for multi-agent systems, including hierarchical, peer-to-peer, and sequential orchestration models, guiding teams on architecture pattern selection

• Architect integrated memory systems covering session memory, long-term knowledge using vector databases and knowledge graphs, and audit or episodic memory strategies

• Design end-to-end Retrieval-Augmented Generation (RAG) and Cache-Augmented Generation architectures, ensuring strong standards for evaluation, observability, lineage, and performance optimization

• Establish cross-cloud and vendor integration frameworks, advising on platform selection while balancing interoperability, scalability, and vendor lock-in considerations

• Define GenAIOps architectural standards including CI/CD, infrastructure as code, monitoring, and performance metrics such as latency, cost, hallucination risk, and token consumption

• Architect enterprise AI safety, security, and governance guardrails, including prompt security, PII protection, bias mitigation, and human-in-the-loop workflows aligned with industry frameworks

• Design scalable integration patterns connecting agentic AI systems with enterprise platforms, identity services, collaboration tools, and cloud-native infrastructure

• Provide senior technical leadership and strategic advisory to executive stakeholders, facilitating workshops, shaping AI roadmaps, and mentoring engineering and architecture teams

• Contribute to industry thought leadership through conference presentations, publications, and active participation in the AI community.

REQUIREMENTS:

• 8–10+ years of experience in technical leadership roles with strong foundations in software engineering and enterprise cloud architecture

• Deep architectural expertise in at least one major cloud platform (Azure, AWS, or GCP) with working knowledge of additional cloud ecosystems

• Proven experience designing and delivering GenAI and LLM-based solutions using enterprise AI platforms

• Demonstrated expertise building complex RAG pipelines and orchestration architectures

• Hands-on experience with LLM instruction tuning or model fine-tuning approaches

• Strong stakeholder engagement skills with experience advising senior leadership and C-level executives on technology strategy and governance

• Experience designing or prototyping advanced multi-agent systems and collaborative AI architectures

• Multi-cloud architectural expertise across Azure, AWS, and GCP environments

• Practical experience with GenAIOps tooling, AI governance frameworks, and AI FinOps or model routing strategies

• Development experience with orchestration frameworks such as LangChain, LlamaIndex, or Semantic Kernel

• Demonstrated thought leadership through publications, conference speaking, patents, or open-source contributions

• Professional cloud certifications such as Azure Solutions Architect Expert, AWS Solutions Architect Professional, or GCP Professional Cloud Architect.

BENEFITS:

• Opportunity to shape enterprise-scale AI transformation programs at the forefront of Agentic AI innovation.

• Exposure to senior executive stakeholders and high-impact global AI initiatives.

• Collaborative environment focused on innovation, technical excellence, and continuous learning.

• Career growth through leadership opportunities, industry visibility, and thought leadership engagement.

• Competitive compensation package aligned with senior-level expertise and impact.