Applied AI Technical Lead

The organization is seeking an experienced Applied AI Engineering Lead to drive the design, development, and implementation of enterprise-scale Agentic AI solutions. The role will lead the adoption of Large Language Models (LLMs), multi-agent systems, intelligent automation, and cloud-native AI architectures to modernize business processes, accelerate knowledge management, enhance incident management capabilities, and improve operational efficiency.

The successful candidate will work closely with product managers, enterprise architects, business stakeholders, and engineering teams to transform strategic objectives into scalable AI-powered solutions.

Objectives

The primary objective of this assignment is to design and deliver enterprise-grade Agentic AI platforms and intelligent automation solutions that:

• Improve operational efficiency through AI-driven workflows.
• Enable intelligent knowledge discovery and content generation.
• Support incident detection, diagnosis, and remediation processes.
• Accelerate digital transformation initiatives across business functions.
• Establish AI engineering standards, governance, and best practices.

Scope of Work

AI Strategy & Solution Architecture

• Design enterprise AI architectures leveraging LLMs, Agentic AI frameworks, Retrieval-Augmented Generation (RAG), MCP Servers, Semantic Kernel, and multi-agent orchestration platforms.
• Define AI solution patterns, technical standards, and governance frameworks.
• Develop scalable, secure, and responsible AI architectures aligned with enterprise policies.

Agentic AI Solution Development

• Design and implement autonomous AI agents capable of planning, reasoning, orchestrating tasks, and interacting with enterprise systems.
• Build intelligent workflows for incident management, document processing, knowledge management, customer support, and operational automation.
• Develop prompt engineering, context engineering, memory management, and orchestration strategies to optimize AI performance.

Cloud & DevOps Enablement

• Architect and deploy AI solutions on cloud platforms such as Microsoft Azure.
• Implement CI/CD pipelines, infrastructure-as-code, monitoring, observability, and AI operations (AIOps) capabilities.
• Optimize cloud resources for performance, scalability, and cost efficiency.

Leadership & Capacity Building

• Lead multidisciplinary engineering teams across onshore and offshore locations.
• Mentor engineers and architects in Agentic AI patterns, cloud-native engineering, and modern software development practices.
• Establish communities of practice and standards for AI engineering excellence.

• Master’s degree in Computer Science, Information Technology, Data Science, Artificial Intelligence, Engineering, or related discipline.
• Minimum 10 years of professional experience in software engineering and enterprise solution delivery.
• Minimum 5 years of experience in cloud architecture and modern application development.
• Demonstrated experience delivering AI/ML and Generative AI solutions in large enterprises.
• Proven experience leading cross-functional engineering teams.
• Experience delivering solutions using Agile and Scrum methodologies.

Technical Expertise

Artificial Intelligence

• Large Language Models (GPT, Claude, Gemini, Azure OpenAI, or equivalent).
• Agentic AI architectures.
• Multi-agent systems.
• Prompt Engineering and Context Engineering.
• Retrieval-Augmented Generation (RAG).
• Semantic Kernel, LangGraph, AutoGen, CrewAI, MCP, or equivalent frameworks.
• NLP, sentiment analysis, classification, summarization, and knowledge management. xrbllit