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AI Engineering Lead – LLM Engineering, AI Development & Intelligent Automation

Boundless
Dubai, UAE
Full Time
Lead
2 weeks ago
LLM Application DevelopmentPrompt EngineeringRAG SystemsAgentic ArchitecturesPythonTypeScript
Free

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LLM Application DevelopmentPrompt EngineeringRAG Systems
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Company Overview

  • Our client, a global and rapidly growing financial services group, operates across international markets through a technology driven, regulated business model.
  • The organisation is investing significantly in Artificial Intelligence, advanced analytics, intelligent automation, and next generation software engineering capabilities.

Role Overview

  • We are seeking an experienced AI Engineering Lead to establish and drive the organisation’s LLM Engineering capability and accelerate AI powered software development across multiple technology teams.
  • This role will define the standards, architecture, tooling, governance, and development practices required to build secure, scalable, production ready AI solutions.
  • You will guide engineering teams in adopting modern AI assisted development workflows, leveraging frontier LLMs, agentic architectures, enterprise RAG systems, and intelligent automation platforms.

AI Engineering Leadership & Delivery

  • Lead the design, development, and implementation of enterprise grade LLM applications, AI agents, intelligent automation solutions, and AI native software products.
  • Guide and mentor engineering teams on AI architecture, prompting strategy, agent design, model selection, code quality, and production deployment.
  • Conduct AI solution design and technical architecture reviews before development begins.
  • Establish engineering standards for secure, scalable, maintainable, and measurable AI implementations.
  • Support teams in adopting AI first development methodologies while maintaining strong software engineering quality, security, and delivery discipline.

AI Assisted Software Engineering

  • Champion the use of AI coding and development environments including Claude Code, OpenAI Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, and emerging AI engineering tools.
  • Define enterprise standards for AI assisted software engineering, developer prompting, code generation, review workflows, and responsible use of AI development tools.
  • Develop reusable prompt libraries, engineering playbooks, technical documentation, and best practice frameworks.
  • Introduce practical AI assisted development workflows that improve engineering productivity, code quality, testing, documentation, and delivery speed.
  • Establish methods to measure and improve developer productivity through AI enabled engineering practices.

LLM Engineering & Agentic Systems

  • Design and optimise production grade LLM applications using leading models including Claude, OpenAI, Gemini, Mistral, Llama, DeepSeek, and other suitable providers.
  • Build AI agents and multi agent workflows using frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, Haystack, and LlamaIndex.
  • Design and implement enterprise RAG systems using vector databases, knowledge graphs, structured data sources, and internal documentation repositories.
  • Develop scalable MCP servers, tool integrations, API connectors, and secure AI access layers for enterprise systems.
  • Evaluate new LLMs, frameworks, AI platforms, and engineering tools, making recommendations for adoption based on technical fit, security, cost, performance, and business value.

AI Platform, Architecture & Governance

  • Define enterprise AI architecture standards, reference architectures, reusable components, and internal AI platform capabilities.
  • Design secure integrations between LLMs, internal systems, APIs, databases, workflows, and customer facing applications.
  • Establish guardrails, AI evaluation frameworks, observability standards, model monitoring, prompt testing, and responsible AI practices.
  • Lead technical decision making around AI infrastructure, deployment patterns, cloud architecture, model hosting, orchestration, and scalability.
  • Support the development of AI governance frameworks covering data privacy, security, access controls, explainability, quality assurance, and risk management.

Capability Development & Adoption

  • Develop an AI Engineering Maturity Model to assess and improve AI adoption across engineering teams.
  • Deliver internal workshops, technical training sessions, and practical enablement programmes for developers, architects, and engineering leaders.
  • Train teams on LLM engineering workflows, AI coding assistants, agentic architectures, RAG implementation, and AI observability practices.
  • Create and maintain internal documentation, coding standards, architecture guidelines, and engineering playbooks.
  • Stay current with emerging AI technologies and continuously identify opportunities to strengthen the organisation’s AI engineering capability.

Requirements

  • Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Software Engineering, or a related technical discipline.
  • At least 7 years of software engineering experience, including strong exposure to software architecture, enterprise applications, APIs, cloud platforms, and modern software delivery practices.
  • At least 3 years of practical experience designing and deploying production LLM applications, AI agents, RAG systems, or intelligent automation solutions.
  • Demonstrated experience leading AI engineering initiatives across multiple engineering teams or business units.
  • Strong hands on knowledge of LLM application development, prompt engineering, RAG, agentic architectures, MCP, model evaluation, orchestration frameworks, and AI observability.
  • Experience with agent frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, Haystack, or similar technologies.
  • Experience working with vector databases and knowledge systems such as Pinecone, Weaviate, pgvector, Qdrant, Chroma, or similar tools.
  • Strong programming ability in Python and ideally TypeScript, with experience developing APIs using FastAPI or similar frameworks.
  • Strong understanding of REST APIs, Docker, Kubernetes, Git, CI/CD, cloud deployment, and enterprise software integration.
  • Experience with AWS, Microsoft Azure, Google Cloud Platform, or multi cloud environments.
  • Familiarity with AI observability and evaluation tools such as LangSmith, Helicone, MLflow, Weights & Biases, OpenTelemetry, or similar platforms.
  • Proven ability to mentor engineers, influence technical direction, establish standards, and communicate complex AI concepts to both technical and non technical stakeholders.

Preferred Experience

  • Building or leading AI Engineering Centres of Excellence.
  • Establishing enterprise AI governance, responsible AI, and AI risk management frameworks.
  • Driving AI transformation programmes across software engineering organisations.
  • Defining AI coding standards, AI SDLC frameworks, and AI assisted development methodologies.
  • Building enterprise LLM platforms, internal AI tools, reusable AI components, or shared AI services.
  • Implementing enterprise RAG, multi agent architectures, MCP servers, and AI workflow automation.
  • Measuring productivity, quality, adoption, and delivery improvements resulting from AI enabled software development.
  • Experience within financial services, fintech, trading, payments, regulated technology environments, or large scale enterprise platforms.

Key Technology Areas

  • Frontier LLMs: Claude, OpenAI GPT models, Gemini, Mistral, Llama, DeepSeek
  • AI Development Tools: Claude Code, Cursor, GitHub Copilot, Gemini CLI, Windsurf, OpenAI Codex, Continue.dev
  • Agent Frameworks: LangGraph, LangChain, CrewAI, AutoGen, Semantic Kernel, Haystack
  • Automation: n8n, Make, Zapier, Apache Airflow
  • Knowledge Systems: Pinecone, Weaviate, pgvector, Qdrant, Chroma, LlamaIndex
  • Engineering: Python, TypeScript, FastAPI, REST APIs, Docker, Kubernetes, Git, CI/CD
  • Cloud Platforms: AWS, Azure, Google Cloud Platform
  • AI Observability: LangSmith, Helicone, MLflow, Weights & Biases, OpenTelemetry

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