Engineering Lead - AI & Enterprise Platform

Key Responsibilities: Engineering Leadership: - Lead, mentor, and manage multiple engineering teams across backend, frontend, AI/ML, platform engineering, DevOps, quality assurance, and data engineering. - Build a high-performance engineering culture focused on ownership, speed, innovation, quality, and customer impact. - Define team structures, engineering responsibilities, development processes, and technical governance frameworks. - Recruit, onboard, develop, and retain high-quality engineering talent. - Conduct regular technical reviews, performance discussions, and development planning for engineering team members. - Develop technical leads and senior developers into strong technology leaders. - Ensure effective collaboration between engineering, product, AI, design, customer success, sales, and implementation teams. Technology Strategy and Architecture: - Work closely with the founders to define the companys long-term technology strategy and product architecture. - Translate business objectives and product requirements into scalable technology roadmaps. - Own the overall architecture of mple.ais enterprise platform, AI systems, integrations, and data infrastructure. - Make architectural decisions related to scalability, performance, reliability, security, maintainability, and cost optimisation. - Establish architecture standards, coding guidelines, documentation practices, and engineering best practices. - Evaluate and implement appropriate technologies, platforms, frameworks, and third-party services. - Balance speed of execution with long-term platform stability and technical sustainability. - Identify and address technical debt while ensuring timely product delivery. AI, LLM and Machine Learning Platforms: - Lead the development and integration of AI, machine learning, generative AI, and LLM-based capabilities. - Build and scale AI-driven applications such as conversational AI, AI coaching, simulations, digital avatars, intelligent assessments, recommendations, analytics, and content generation. - Guide the engineering of LLM-based workflows, including prompt orchestration, retrieval-augmented generation, embeddings, vector databases, agentic workflows, model evaluation, and guardrails. - Oversee integration with commercial and open-source AI models. - Establish frameworks for model selection, testing, accuracy evaluation, hallucination reduction, observability, and continuous improvement. - Ensure AI features meet enterprise standards for security, privacy, explainability, performance, and responsible use. - Work closely with AI/ML engineers and data scientists to move prototypes into secure, production-ready systems. - Optimise AI infrastructure for latency, reliability, model performance, and cost. Product Development and Scaling: - Lead the development of enterprise products from early-stage validation to large-scale adoption. - Demonstrate experience in taking products from the 1 to 10 stage by improving architecture, processes, teams, and operational maturity. - Build scalable platforms capable of supporting multiple enterprise customers, industries, languages, geographies, and user roles. - Ensure products are modular, configurable, integration-friendly, and suitable for enterprise deployment. - Oversee the complete software development lifecycle, from product discovery and architecture to development, testing, deployment, and production support. - Establish release planning, sprint governance, delivery tracking, and engineering performance metrics. - Improve product stability, application performance, uptime, deployment frequency, and engineering productivity. - Build systems that can scale across increasing users, transactions, content, AI workloads, and enterprise customers. Enterprise Product Engineering: - Build secure, configurable, multi-tenant, enterprise-grade SaaS products. - Design systems that support role-based access, customer-specific configurations, workflows, analytics, reporting, and administrative controls. - Lead enterprise integrations with CRM, LMS, HRMS, SSO, identity platforms, communication systems, data warehouses, and third-party APIs. - Ensure compatibility with enterprise requirements such as SSO, SAML, OAuth, API security, audit logs, data residency, access controls, and customer-specific deployment needs. - Partner with implementation and customer teams to understand enterprise requirements and convert them into reusable product capabilities. - Support technical discussions, solutioning, architecture reviews, and due diligence with enterprise customers. - Participate in key client meetings where technology, integration, security, or product architecture expertise is required. Security Architecture and Compliance: - Own the security architecture of the platform, applications, infrastructure, APIs, databases, and AI systems. - Establish secure software development lifecycle practices across engineering teams. - Implement strong identity and access management, encryption, logging, monitoring, vulnerability management, and data protection controls. - Ensure security is embedded into architecture and development processes from the design stage. - Oversee application security, infrastructure security, cloud security, API security, and data privacy. - Manage security reviews, vulnerability assessments, penetration testing, remediation, and security audits. - Build systems aligned with enterprise security expectations and applicable standards such as ISO 27001, SOC 2, GDPR, and relevant data-protection requirements. - Ensure appropriate handling of confidential enterprise data, personal information, AI training data, and customer-generated content. - Develop disaster recovery, business continuity, backup, incident response, and risk-management practices. - Work with external auditors, security consultants, enterprise IT teams, and customer security stakeholders. Cloud, DevOps and Reliability: - Oversee cloud infrastructure, deployment architecture, DevOps, observability, and site reliability. - Build scalable cloud-native systems using appropriate microservices, containers, serverless, or modular architecture. - Establish effective CI/CD pipelines, automated testing, infrastructure-as-code, and release-management practices. - Improve platform uptime, system monitoring, application performance, and incident response. - Define service-level objectives and engineering standards for availability, latency, reliability, and recovery. - Optimise cloud and AI infrastructure costs without compromising performance or security. - Ensure production environments are stable, observable, auditable, and resilient. - Develop processes for root-cause analysis, production incident management, and preventive actions. Engineering Delivery and Governance: - Own engineering delivery against product and business priorities. - Convert strategic objectives into clear engineering plans, milestones, ownership, and timelines. - Create visibility into project status, engineering risks, dependencies, capacity, and delivery performance. - Establish measurable engineering KPIs such as release velocity, defect rates, uptime, cycle time, deployment frequency, code quality, and incident resolution. - Identify delivery bottlenecks and implement process improvements. - Ensure timely communication of technical risks, trade-offs, delays, and dependencies to founders and business leaders. - Maintain appropriate technical documentation, architecture diagrams, API documentation, runbooks, and system-design records. - Build predictable engineering processes while preserving the agility required in a fast-growing company. Required Qualifications: - 1218 years of overall experience in software engineering and technology leadership. - Significant experience in engineering management, technology leadership, or platform leadership roles. - Proven experience leading