Description

Key Skills: Agentic AI, AWS, LLM, Lang chain, Azure, Lang graph, GenAI Architect, DevOps, CICD, Containerization (Docker, Kubernetes)

Roles and Responsibilities:

• Lead the architecture and development of production-grade Generative AI, Agentic AI, RAG, and intelligent automation solutions.
• Evaluate business and technical requirements to select appropriate Azure, AWS, no-code, low-code, or pro-code implementation approaches.
• Design scalable, secure, maintainable AI architectures and establish reusable engineering patterns for enterprise adoption.
• Lead technical design reviews, technology evaluations, solution governance, and architecture decisions across distributed engineering teams.
• Guide AI solutions through discovery, development, evaluation, deployment, monitoring, and continuous improvement while mentoring engineers.

Skills Required:

• 7 - 14 years of software engineering experience, including significant experience architecting and leading production AI or intelligent automation solutions.
• Hands-on experience with Microsoft Azure and AWS for enterprise application and AI solution delivery.
• Strong experience designing Agentic AI and LLM-based applications, including AI orchestration and tool-calling workflows.
• Proficiency with LangChain and LangGraph for building AI workflows, agents, and production-grade orchestration solutions.
• Experience defining Generative AI architecture, including RAG, evaluation, guardrails, observability, security, and responsible AI controls.

Good to Have:

• DevOps and CI/CD experience.
• Containerization experience with Docker and Kubernetes.

Education: Bachelor's or master's degree in Computer Science, Information Technology, Engineering, or a related discipline

Key Skills

Education

Any Graduate