• Implementing and optimizing AI-driven solutions to improve internal operations and workflows, under the strategic guidance of the Head of Internal AI Solutions; • Translating strategic objectives into functional software applications using specific technologies; • Handling both front-end and back-end development tasks, ensuring seamless integration and performance across our tech stack; • Proactively identifying and resolving technical challenges to enhance system functionality and user experience.

About GBG Enabling safe and rewarding digital lives for genuine people, everywhere We make it our mission to ensure more genuine people have digital access to opportunities, and businesses have access to more genuine people. Our technology draws on diverse and reliable data to create a single point of truth for identity and address verification. With over 30 years of experience behind us our team and technology are focused on enabling safe and rewarding digital lives for everyone. Regardless of age, location or background, genuine people everywhere should be able to digitally prove who they are and where they live. About the team and role CVML Teams At the heart of GBG's Documents and Biometrics portfolio, our team focuses on creating unique and powerful artificial intelligence models. These models are designed to revolutionize KYC verification for our customers. We drive the development of these cutting-edge technologies, aiming to provide unparalleled solutions for document verification and digital trust. Collaboration is our cornerstone as we bring together diverse expertise to achieve collective success. Guided by Agile methodology, our daily operations focus on efficiency through automation. Director – Machine Learning The Director of Machine Learning provides strategic, technical, and people leadership for machine learning initiatives across the Documents & Biometrics organization. This role is accountable for defining the long‑term AI/ML vision and roadmap, translating business and product strategy into impactful ML capabilities, and ensuring the reliable, ethical, and scalable delivery of ML models into production. The Director operates as both a senior technical authority and an organizational leader, driving innovation, mentoring teams, influencing stakeholders, and ensuring that machine learning efforts deliver measurable customer and business value. This role requires deep expertise in machine learning and computer vision, strong leadership capability, and the ability to operate effectively across product, engineering, operations, compliance, and executive leadership functions.. What you will do Strategic Leadership & Vision - Define, own, and execute the long‑term AI and Machine Learning strategy for the Documents & Biometrics domain, aligned with company objectives and product roadmaps. - Identify opportunities where machine learning can materially improve classification, extraction, fraud detection, image processing, and overall product performance. - Serve as a thought leader for AI/ML within the organization, advocating for modern approaches, emerging technologies, and best practices. Technical & Delivery Leadership - Provide hands‑on technical leadership across the full ML lifecycle, including research, model design, experimentation, validation, deployment, and continuous improvement. - Raise the bar for technical excellence while fostering an inclusive, high‑engagement team culture. - Oversee the development and productization of ML models addressing real‑world document and biometric challenges at scale. - Establish and evolve robust MLOps practices to ensure reproducibility, reliability, observability, cost effectiveness, and consistent high‑quality model delivery. - Ensure the availability, quality, and scalability of labeled data pipelines necessary to support ongoing model development and accuracy improvement. People & Team Leadership - Lead, mentor, and develop a team of senior machine learning engineers and technical leaders, fostering a culture of trust, accountability, collaboration, and continuous learning. - Build high‑performing teams that balance innovation with operational excellence. - Set clear expectations, provide regular feedback, and support the professional growth and progression of team members. - Builds trust through transparency, technical credibility, and consistent delivery. Cross‑Functional Collaboration - Partner closely with Product Management to define AI/ML roadmaps, prioritize initiatives, and ensure timely and high‑impact delivery. - Collaborate effectively with Engineering, Architecture, Data, Platform, Security, Legal, and Compliance teams to ensure ML systems are scalable, secure, and compliant. - Represent Documents & Biometrics in cross‑company forums related to AI strategy, governance, and innovation. Governance, Ethics & Compliance - Ensure that machine learning systems are developed and operated in accordance with applicable AI governance frameworks, regulatory requirements, and ethical best practices. - Contribute to company‑wide AI governance efforts, including AI risk assessment, documentation, explainability, and stakeholder readiness. - Promote appropriate AI literacy within the team and ensure responsible design and use of ML technologies. Operational Excellence - Manage multiple complex initiatives simultaneously, balancing innovation, delivery commitments, and operational stability. - Ensure adherence to industry best practices, architectural standards, and engineering quality bars. - Maintain high levels of team morale, engagement, and delivery velocity. Skills we're looking for - PhD in AI, Machine Learning, Computer Science, or a related field, or equivalent depth of industry experience. - Deep technical expertise in machine learning, computer vision, and deep learning applied to real‑world, production systems. - 10+ years of hands‑on experience in machine learning and computer vision, with a substantial portion in leadership roles. - Significant experience leading and scaling machine learning teams in a product‑focused environment. - Proven track record of delivering ML solutions end‑to‑end, from concept through production and ongoing optimization. - Strong experience building and operating MLOps pipelines, data workflows, and production ML systems. - Demonstrated ability to influence across organizational boundaries and communicate effectively with both technical and non‑technical stakeholders. - Experience operating in highly dynamic, fast‑moving environments with competing priorities. - Experience with regulated environments, AI governance frameworks, or compliance‑driven ML development would be beneficial - Experience delivering ML solutions with measurable customer or business impact at scale To find out more As an equal opportunity employer, we are dedicated to creating a diverse and inclusive workplace where everyone feels valued and empowered. Please inform your GBG Talent Attraction Partner if you require any reasonable adjustments to the interview process. To chat to the Talent Attraction team and find out more about our benefits and why we’re a great place to work, drop an email to behired@gbgplc.com and we’ll be in touch. You can also find out more about careers at GBG and check out our current opportunities at gbgplc.com/careers. Unleash your potential and be part of our mission to power safe and rewarding digital lives.

• Craft clean, testable, and maintainable code to enable AI-generated agent-based models. • Own the software from requirements development through deployment and maintenance that enable decision makers to generate agent-based models that address critical business questions and data scientists to build agent-based models more quickly that answer the questions of decision makers. • Design, build, test, and deploy a scalable system architecture so that AI-generated models can be validated by data scientists and deliver results back to decision makers quickly. • Own the engineering solution and collaborate with internal teams to ensure alignment with company strategy.

Role Description - Design and build agentic AI systems, including autonomous agents, multi-agent orchestration, workflow state machines, and tool-using agents. - Develop LLM-driven agents capable of reasoning, planning, retrieval (RAG), and task execution across enterprise systems. - Build and maintain AI-powered automation workflows using platforms like n8n and Make to orchestrate business processes and cross-application integrations. - Integrate agents with APIs, CRM/ERP systems, collaboration tools, databases, and payment platforms using tool/function calling, MCP, and A2A patterns. - Implement robust execution logic (validation, retries, rate limits, fallbacks, error handling) to ensure reliability and scalability. - Design and manage RAG pipelines using embeddings, vector databases, chunking, and reranking strategies. - Establish safety guardrails, access controls, and human-in-the-loop workflows for high-risk actions. - Build evaluation, observability, and tracing pipelines to monitor performance, cost, latency, and reliability. - Deploy and operate agent services in cloud environments (AWS, Azure, or GCP) using Docker, Kubernetes, Terraform, and CI/CD. - Monitor production systems, troubleshoot issues, and continuously improve agent performance and policies. - Prototype and benchmark emerging agentic AI frameworks and models. - Create technical documentation and communicate AI solutions effectively to cross-functional stakeholders. Qualifications - Bachelor’s or Master’s degree in Computer Science, AI, Engineering, or related field. - 3+ years of software engineering experience (Python and/or TypeScript). - 1+ year building LLM-powered or agentic AI systems in production or near-production environments. - Experience with agent frameworks (e.g., LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel). - Hands-on experience with automation/orchestration tools (e.g., n8n, Make) in production settings. - Strong understanding of LLMs, embeddings, prompt engineering, structured outputs, and tool calling. - Experience designing REST APIs, microservices, and backend systems. - Familiarity with vector databases and RAG architectures. - Experience with cloud platforms (AWS, Azure, or GCP), containerization (Docker, Kubernetes), and infrastructure-as-code tools. - Strong system design, debugging, and communication skills. Requirements - Experience with MCP, A2A, or advanced agent communication patterns. - Advanced experience with n8n (custom nodes, self-hosting) or Make (complex scenarios). - Experience combining LLMs with workflow engines for document processing, reporting, chatbots, or decision support. - Familiarity with AI evaluation and observability tools (e.g., LangSmith, OpenAI Evals, Weights & Biases). - Experience with multi-agent systems, planning algorithms, RL, fine-tuning, or RLHF. - Knowledge of CI/CD pipelines and security best practices. - Experience in regulated industries (e.g., healthcare, finance, defense). - Relevant cloud or ML certifications. Company Description Dear recruiters there is no need to edit this.