Lead ai engineer

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.

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. 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. 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. 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 (Po C) 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. 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. Technical Skills Programming Languages Python Type Script Backend Technologies Fast API Node.js Nest JS REST APIs Cloud & AI Platforms Microsoft Azure AI Services Azure Open AI Azure Functions Azure App Services Open AI 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 Lang Graph Lang Chain Semantic Kernel Auto Gen Similar orchestration frameworks Security OAuth Role-Based Access Control (RBAC) Identity Management API Security Secrets Management Dev Ops Git Git Hub Azure Dev Ops CI/CD Pipelines Monitoring & Observability 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. 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) 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 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 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)