Overview

We are seeking an R&D Lead to drive applied research and technology innovation for large-scale enterprise software solutions.

Responsibilities

• Lead applied research in Generative AI, agentic workflows, machine learning, intelligent automation and other emerging enterprise technologies, prioritizing practical use cases.
• Assess LLMs and SLMs, retrieval-augmented generation (RAG), semantic search, document intelligence, natural-language interfaces and AI-assisted decision support for integration into enterprise applications.
• Design and build proofs of concept, prototypes, reference implementations and benchmarks; validate feasibility, accuracy, security, performance, cost and operational suitability.
• Transform validated ideas into reusable APIs, libraries, frameworks, components and development accelerators that can be adopted across multiple software projects.
• Guide AI integration with existing business workflows, rules engines, databases, document repositories, identity systems and enterprise APIs.
• Evaluate open-source frameworks and emerging architectures for maturity, licensing, community health, maintainability, interoperability and total cost of ownership.
• Explore modern application architecture, data engineering, analytics, enterprise search, cloud-native platforms and automation to improve solution quality and delivery efficiency.
• Architect AI deployment for controlled environments, including on-premises data centres and approved cloud infrastructure, with appropriate model hosting, inference, access controls and observability.
• Apply responsible AI engineering practices, including model evaluation, grounding, human oversight, privacy safeguards, auditability, and LLMOps/MLOps where relevant.
• Investigate AI-assisted engineering practices for coding, testing, documentation, migration and operations; measure productivity and quality improvements.
• Own an outcome-driven R&D roadmap, collaborate with solution architects and project teams, and guide successful handover of validated technologies into delivery.
• Mentor technical staff, document technical decisions and findings, and present demonstrations, recommendations and adoption plans to leadership.
• Strong programming and engineering skills in Python, Java or comparable enterprise technologies, with sound understanding of APIs, integration patterns and modular/distributed architectures.
• Practical knowledge of LLMs/SLMs, RAG, embeddings, vector search, tool calling, agent orchestration and AI evaluation; experience with relevant frameworks such as LangChain, LangGraph, LlamaIndex or equivalents.
• Understanding of model serving, self-hosted inference, retrieval pipelines, fine-tuning trade-offs and model lifecycle practices.
• Working knowledge of relational databases, enterprise search, data processing, secure software development and application observability.
• Experience with Linux, containers, Kubernetes, CI/CD and deployment of reliable enterprise applications in cloud or on-premises environments.
• Ability to compare technical alternatives using measurable criteria such as accuracy, throughput, latency, scalability, security, maintainability and cost.
• Production implementation of AI-enabled workflows, conversational interfaces, intelligent document processing or enterprise knowledge retrieval.
• Private or self-hosted AI deployments, GPU inference, open-source model ecosystems, or regulated and security-sensitive infrastructure.
• Experience with large, mission-critical enterprise platforms involving complex workflows, substantial data volumes, multiple integrations and high availability.
• Contribution to reusable internal platforms, engineering accelerators, reference architectures or open-source projects.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Information Technology, Software Engineering or a related discipline.
• 7+ years in software engineering, enterprise application development, solution architecture or applied R&D, including experience leading technical initiatives.
• Substantial hands-on experience developing AI-enabled applications or integrating AI/ML capabilities into software solutions; recent practical work with modern Generative AI is strongly preferred.
• Proven ability to take technology initiatives from evaluation and prototyping to validated, production-oriented implementation.
• Strong communication, technical decision-making, mentoring and cross-functional collaboration skills.

Benefits

• A prioritized R&D roadmap linked to real software delivery needs and measurable business or engineering value.
• Validated AI capabilities and reusable components adopted by project teams rather than remaining isolated demonstrations.
• Documented benchmarks, technical standards and implementation guidance supporting sustainable production deployment.
• Demonstrable improvements in solution capability, engineering productivity, reliability or implementation efficiency.