The Lead AI Engineer is responsible for leading the technical design, architecture, engineering standards, and delivery of enterprise-grade Artificial Intelligence (AI) solutions. This role serves as the technical authority for Large Language Model (LLM) applications, Retrieval-Augmented Generation (RAG), intelligent agents, AI orchestration, model evaluation, responsible AI practices, and production-ready AI implementations.

The successful candidate will define AI engineering standards, mentor development teams, and ensure AI solutions are secure, scalable, compliant, and aligned with business objectives.

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Key Responsibilities

1. AI Engineering Leadership

• Define engineering standards, best practices, and architecture patterns for enterprise AI applications.
• Lead technical design reviews to ensure AI solutions meet security, architecture, scalability, and governance requirements.
• Develop reusable frameworks and blueprints for:
  • Retrieval-Augmented Generation (RAG)
  • AI agents
  • Workflow automation
  • Knowledge assistants
  • AI orchestration
• Mentor AI engineers and software developers while promoting engineering excellence.
• Translate business strategy into technical roadmaps and implementation plans.

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2. Solution Architecture & Technical Delivery

• Design end-to-end AI solutions covering:
  • Frontend applications
  • Backend services
  • APIs
  • AI orchestration
  • LLM integration
  • Enterprise data retrieval
  • Security
  • Monitoring
• Lead the delivery of AI-powered business applications including:
  • Intelligent knowledge assistants
  • HR automation
  • Finance automation
  • AI-powered software development tools
  • Voice and conversational AI
  • Workflow automation
• Evaluate and recommend appropriate AI platforms, models, and frameworks based on business requirements.
• Review and approve solution architecture, integration patterns, deployment strategies, and technical documentation.
• Ensure AI applications are modular, scalable, maintainable, and API-first.

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3. AI, LLM & RAG Engineering

• Design enterprise Retrieval-Augmented Generation (RAG) architectures.
• Implement secure retrieval pipelines using vector databases, embeddings, search indexing, and enterprise knowledge repositories.
• Design intelligent AI agents capable of:
  • Task planning
  • Tool execution
  • Enterprise system integration
  • Human-in-the-loop workflows
• Develop standards for:
  • Prompt engineering
  • Prompt lifecycle management
  • AI evaluation
  • Model governance
• Establish evaluation frameworks measuring:
  • Accuracy
  • Relevance
  • Hallucination risk
  • Response latency
  • Operational cost
  • User satisfaction
• Ensure AI outputs remain explainable, traceable, and aligned with Responsible AI principles.

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4. Governance, Security & Risk

• Embed Responsible AI, privacy, security, and governance into every AI solution.
• Collaborate with cybersecurity, compliance, legal, and risk teams to identify and mitigate AI-related risks.
• Define governance checkpoints for:
  • Proof of Concept (PoC)
  • Pilot deployment
  • Production release
  • Ongoing monitoring
• Ensure compliance with internal policies, regulatory requirements, and data privacy standards.
• Maintain architectural documentation, design decisions, and risk assessments.

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5. Stakeholder & Vendor Management

• Work closely with business stakeholders to define AI requirements and success measures.
• Support build-versus-buy assessments for AI platforms and technology solutions.
• Provide technical leadership to internal engineering teams and external vendors.
• Present technical recommendations to senior leadership and steering committees.
• Act as the primary technical authority for enterprise AI initiatives.

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Technical Skills

Programming Languages

• Python
• TypeScript

Backend Technologies

• FastAPI
• Node.js
• NestJS
• REST APIs

Cloud & AI Platforms

• Microsoft Azure AI Services
• Azure OpenAI
• Azure Functions
• Azure App Services
• OpenAI APIs
• Anthropic Claude (or equivalent LLM platforms)

AI Technologies

• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• AI Agents
• Vector Databases
• Embeddings
• Prompt Engineering
• Prompt Governance
• AI Evaluation Frameworks

AI Frameworks

• LangGraph
• LangChain
• Semantic Kernel
• AutoGen
• Similar orchestration frameworks

Security

• OAuth
• Role-Based Access Control (RBAC)
• Identity Management
• API Security
• Secrets Management

DevOps

• Git
• GitHub
• Azure DevOps
• CI/CD Pipelines
• Monitoring & Observability

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Qualifications & Experience

• Bachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, Information Technology, or a related discipline.
• Minimum 8 years of experience in software engineering, solution architecture, AI engineering, or platform engineering.
• Minimum 3 years of experience designing and deploying enterprise AI, machine learning, or automation solutions.
• Demonstrated experience leading technical teams or acting as a technical authority.
• Experience delivering enterprise software within complex organizational environments.
• Experience with cloud platforms, preferably Microsoft Azure.
• Strong experience integrating enterprise systems using secure APIs.

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Preferred Qualifications

The following experience would be advantageous:

• AI or Cloud certifications
• Microsoft Azure certifications
• AI Engineering certifications
• Enterprise Architecture certifications
• Experience with:
  • Financial Services
  • Banking
  • Telecommunications
  • Digital Transformation
  • Workflow Automation
  • Conversational AI
  • Voice AI
  • Contact Centre AI
  • Responsible AI Governance
  • AI Operations (LLMOps / MLOps)

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Core Competencies

• Enterprise AI Architecture
• Solution Design
• Technical Leadership
• AI Engineering
• Strategic Thinking
• Stakeholder Management
• Communication Skills
• Problem Solving
• Innovation
• Decision Making
• Team Leadership
• Coaching & Mentoring
• Adaptability
• Continuous Learning

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Key Performance Indicators (KPIs)

Performance will be measured through:

• Delivery of AI initiatives within agreed timelines
• Production readiness and operational stability
• Reusability of AI frameworks and engineering assets
• AI quality, accuracy, and governance compliance
• Security and risk management
• Application scalability and maintainability
• Cost optimization
• Business stakeholder satisfaction
• Technical leadership and team development

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Career Progression

Potential career pathways include:

• Head of AI Engineering
• AI Solution Architect
• Principal AI Engineer
• AI Platform Lead
• Enterprise Architect
• Director of AI & Automation
• Chief Technology Officer (AI)