Description

Job Description:

1) Rapidly assess the customer landscape, design and implementation by understanding their processes, systems, data, and pain points to identify and implement high-impact GenAI/agentic opportunities.
2) Define and own the end-to-end solution and technical architecture covering model selection, agent design, data ingestions, integration, services, guardrails, evaluation framework, and cost/security posture.
3) Lead the team hands-on through implementation, setting engineering and prompt-engineering standards, unblocking hard problems, and driving an iterative measure-improve-ship cadence.
4) Collaborate with business, IT, security, and engineering stakeholders to manage expectations, communicate risks, align on goals, and keep delivery on track.
5) Own end-to-end delivery and adoption, instrument success metrics, and establish a post-go-live operating model that sustains measurable productivity gains in production.

Required Skills:

1. Architecting, solutioning, and implementing scalable multi-agent orchestrated systems leveraging LLMs, LangChain/LangGraph, ACP, and RAG, with strong capabilities in agent design patterns, workflow orchestration, context/memory engineering, prompt and tool integration, and enterprise-grade observability, governance, and reliability

2. Python, Data Ingestion, Data Pipeline, Vector DB

3. Azure AI Foundry , Azure AI Service (or Similar services)
Years of Experience: 10.00+ Years of Experience

Key Skills

Education

Any Graduate