Overview
As an AI Technical Lead / Architect focused on Generative and Agentic AI, you will architect, design, and lead the delivery of production-ready, enterprise-grade AI systems. You will take ideas from early experimentation and prototypes through scalable, secure, and reliable AI solutions used by real customers and internal users.
The role focuses on building intelligent, agent-based systems that can reason, plan, integrate with tools, and make autonomous decisions at enterprise scale.
Strong engineering mindset with end-to-end ownership, experience delivering real-world AI solutions at enterprise scale, and ability to mentor and influence across teams.
Key Responsibilities
Technical Leadership & Architecture
- Provide technical leadership and architecture for Generative and Agentic AI initiatives
- Design end-to-end AI system architectures covering orchestration, data, APIs, security, and deployment
- Build LLM-based solutions using RAG, embeddings, prompt engineering, function calling, and agentic workflows
- Ensure production readiness including CI/CD, testing, observability, scalability, security, and cost optimization
- Mentor AI engineers and collaborate with business and technology stakeholders
Generative & Agentic AI Engineering
- Design and build LLM-based systems using techniques such as:
- Retrieval Augmented Generation (RAG)
- Embeddings and vector search
- Prompt engineering and prompt optimization
- Function calling and tool usage
- Agentic and multi-agent workflows
- Build AI systems that integrate LLMs with APIs, data platforms, enterprise applications, and workflow engines
- Apply modern AI evaluation approaches including automated testing, benchmarking, and qualitative + quantitative LLM evaluation
Engineering Exc ellence & Operations
- · Apply strong software engineering and distributed systems principles to AI development
- · Design for observability, including logging, metrics, tracing, alerts, and model performance monitoring
- · Diagnose and resolve production issues related to latency, reliability, data quality, and model behavior
- · Balance innovation speed with operational excellence and long-term maintainability
Collaboration, Enablement & Mentorship
- · Work closely with business stakeholders to translate real-world problems into AI-driven solutions
- · Partner with AI engineers, data scientists, and platform teams to scale delivery
- · Mentor and coach engineers on Generative AI, Agentic AI, and cloud-native best practices
- · Support low-code and Copilot/Flow-style solutions where appropriate, while ensuring architectural integrity
