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 multiple teams of developers and technical specialists.

- Strong hands-on understanding of software architecture, product engineering, cloud infrastructure, security, databases, APIs, and distributed systems.

- Experience working in AI, machine learning, generative AI, or LLM-based product environments.

- Practical knowledge of LLM application development, model APIs, embeddings, vector databases, RAG systems, model evaluation, AI guardrails, and AI observability.

- Demonstrated experience building and scaling products from early-stage development to large-scale commercial deployment.

- Proven experience building enterprise-grade SaaS or technology products.

- Experience working closely with founders, particularly technology founders, in a high-growth or entrepreneurial environment.

- Experience managing product architecture, engineering roadmaps, delivery priorities, and technical teams.

- Strong understanding of enterprise security architecture and secure product-development practices.

- Experience handling enterprise integrations, identity management, data protection, cloud security, and compliance requirements.

- Strong analytical, critical-thinking, and problem-solving abilities.

- Ability to solve complex technical, architectural, operational, and customer-related problems.

- Strong communication and stakeholder-management skills.

- Ability to explain complex technical decisions to business leaders, customers, and non-technical stakeholders.

- Bachelors or Masters degree in Computer Science, Information Technology, Engineering, Artificial Intelligence, Data Science, or a related field from a premier institution or recognised university.

Preferred Technical Exposure :

The candidate should have strong exposure to several of the following areas :

- Python, Java, Node.js, Go, or similar backend technologies.

- Modern frontend frameworks such as React, Angular, or Vue.

- AI/ML frameworks and platforms.

- LLM APIs and open-source language models.

- Retrieval-augmented generation and agentic AI workflows.

- Vector databases, embeddings, semantic search, and knowledge systems.

- SQL and NoSQL databases.

- Microservices and event-driven architectures.

- Cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.

- Docker, Kubernetes, serverless computing, and infrastructure-as-code.

- CI/CD pipelines, automated testing, monitoring, and observability tools.

- REST APIs, GraphQL, webhooks, and enterprise integration frameworks.

- Multi-tenant SaaS architecture.

- Identity and access management, SSO, SAML, OAuth, and role-based access control.

- Application security, cloud security, data encryption, audit logging, vulnerability management, and penetration testing.

- Data pipelines, analytics platforms, reporting systems, and enterprise data integrations.