About the Organisation :

Our client is an established organisation in the Education Technology and Learning Solutions sector, focused on making quality education and learning opportunities more accessible through technology.

With a strong presence in the education ecosystem, the organisation leverages digital platforms and technology-driven solutions to address evolving learning needs.

The organisation is expanding its AI capabilities and investing in next-generation Generative AI solutions. This opportunity offers the chance to contribute to enterprise-scale AI initiatives, develop intelligent applications and drive the adoption of Agentic AI and LLM-powered solutions.

Role Overview :

We are looking for an experienced and hands-on Lead GenAI Engineer to drive the architecture, development and deployment of enterprise-grade Generative AI applications.

The ideal candidate should have strong expertise in Python, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI and Multi-Agent architectures.

This role requires an engineer who can take AI solutions beyond prototypes and proof-of-concepts and successfully transition them into scalable, reliable and production-ready systems.

You will be responsible for leading technical implementation, designing AI architectures, optimising model performance, establishing LLMOps/MLOps practices and mentoring engineering teams.

We are looking for someone who combines strong hands-on engineering capabilities with technical leadership and end-to-end solution ownership.

Key Responsibilities :

1. GenAI Architecture & Engineering :

- Lead the GenAI roadmap, taking solutions from prototypes and proof-of-concepts to production.

- Design, develop and deploy enterprise-grade applications powered by Large Language Models.

- Architect and implement Retrieval-Augmented Generation (RAG) pipelines.

- Develop LLM-powered applications with contextual retrieval and intelligent response generation.

- Design scalable and secure GenAI architectures for enterprise use cases.

2. Agentic AI & Multi-Agent Systems :

- Design and develop Agentic AI and Multi-Agent systems.

- Work with frameworks such as LangGraph and Google Agent Development Kit (ADK).

- Implement agent orchestration, tool calling and enterprise system integrations.

- Explore and implement MCP (Model Context Protocol) and A2A (Agent-to-Agent) communication.

- Build intelligent workflows involving multiple specialised AI agents.

3. Edge AI & Model Optimisation :

- Drive Edge AI initiatives using technologies such as Ollama and vLLM.

- Implement model quantisation, distillation and inference optimisation techniques.

- Evaluate efficient model-serving strategies for different deployment environments.

- Optimise inference speed, resource utilisation and model performance.

4. LLMOps, MLOps & Production Deployment :

- Establish LLMOps/MLOps best practices across the GenAI development lifecycle.

- Implement CI/CD pipelines for AI application development and deployment.

- Establish observability, logging, monitoring, testing and AI guardrails.

- Ensure production AI applications meet reliability, scalability and security requirements.

- Continuously monitor and improve deployed models and applications.

5. Performance, Scalability & Reliability :

- Optimise AI systems for scalability, reliability, latency and cost efficiency.

- Identify and resolve performance bottlenecks in LLM applications and retrieval pipelines.

- Design robust architectures capable of supporting enterprise workloads.

- Implement appropriate security controls and production AI best practices.

6. Technical Leadership :

- Provide technical direction and mentorship to engineering teams.

- Conduct architecture reviews, code reviews and technical assessments.

- Establish engineering standards and development best practices.

- Collaborate with stakeholders to translate business requirements into technical solutions.

- Take ownership of end-to-end GenAI engineering initiatives.

Required Technical Skills :

Programming & Development :

- Strong hands-on proficiency in Python.

- Experience developing APIs and production-grade applications.

Generative AI & LLMs :

- Large Language Models (LLMs).

- Retrieval-Augmented Generation (RAG).

- Agentic AI and Multi-Agent Systems.

- Embeddings and Vector Databases.

- LLM-powered application development.

Agentic AI Frameworks & Protocols :

- LangGraph.

- Google Agent Development Kit (ADK).

- Model Context Protocol (MCP).

- Agent-to-Agent (A2A) communication.

Edge AI & Model Serving :

- Ollama.

- vLLM.

- Model quantisation and distillation.

- Inference optimisation.

MLOps / LLMOps :

- CI/CD.

- Observability and monitoring.

- AI guardrails and testing.

- Production deployment and lifecycle management.

Cloud Platforms :

- AWS.

- Google Cloud Platform (GCP).

- Microsoft Azure.

Mandatory Experience & Qualifications :

- Minimum 8+ years of overall professional experience.

- At least 3+ years of hands-on experience building and deploying production GenAI/LLM systems.

- Strong practical experience in Python and LLM-based application development.

- Demonstrated experience implementing RAG pipelines and Agentic AI solutions.

- Hands-on experience with LangGraph and/or Google ADK.

- Understanding of embeddings, vector databases and retrieval architectures.

- Experience working with at least one major cloud platform : AWS, GCP or Azure.

- Practical understanding of LLMOps/MLOps, CI/CD and production AI systems.

- Ability to design technical architectures and provide engineering leadership.

- Strong analytical, problem-solving and communication skills.

What We Are Looking For :

- The ideal candidate should have personally designed, developed and deployed production-grade GenAI applications.

- Be able to explain the architecture, implementation decisions and challenges of systems they have built.

- Possess strong knowledge of RAG architecture, agent orchestration and LLM application development.

- Understand production challenges involving scalability, security, latency, reliability and cost optimisation.

- Be comfortable writing and reviewing code while leading technical initiatives.

- Have the ability to mentor engineers and drive engineering excellence.

Important : This is a hands-on engineering leadership position, not a purely managerial or product-management role. Candidates with only theoretical AI knowledge, general AI/ML exposure or limited POC experience may not be suitable.