Role Description As part of SIDE's Global Player Support organization, the Global Learning & Development Lead is responsible for leading the strategy, delivery, and continuous evolution of learning programs that drive operational excellence across global Player Support teams. This role oversees onboarding, continuous learning, operational readiness, certification, and capability development while leading a team of Learning & Development Specialists. Working closely with Operations, Quality, Workforce Management, Instructional Design, and Client stakeholders, you will ensure learning initiatives are aligned with business objectives, client expectations, and evolving operational needs. The ideal candidate brings strong Learning & Development experience within Player Support, Customer Experience, BPO, or preferably the gaming industry, along with proven people management experience and a passion for developing high-performing learning teams. Qualifications - 5–6 years of Learning & Development experience within Player Support, Customer Experience, Customer Support, BPO, or similar operational environment. - 1–2 years of people management experience, leading Trainers, Learning Specialists, or Learning teams. - Experience within the gaming industry or supporting gaming clients is highly preferred. - Strong understanding of adult learning principles, instructional design, onboarding, operational readiness, and learning evaluation methodologies. - Demonstrated experience building learning strategies, certification programs, governance frameworks, and scalable onboarding programs. - Strong facilitation and presentation skills across both virtual and classroom environments. - Experience using operational data, quality insights, and customer feedback to drive learning interventions. - Excellent stakeholder management skills with the ability to influence leaders across Operations, Quality, Workforce Management, Product, and Client teams. - Experience with Learning Management Systems (LMS), Microsoft Office Suite, SharePoint, and knowledge management platforms. - Experience with e-learning authoring tools such as Articulate Storyline, Rise, Camtasia, Canva, or similar tools is advantageous. Requirements - Lead the planning, execution, and continuous improvement of global onboarding, upskilling, cross-skilling, and continuous learning programs. - Own learning governance, certification frameworks, operational readiness standards, and role-based learning pathways across multiple programs. - Ensure learning solutions support business goals, operational KPIs, client requirements, and player experience objectives. - Oversee Train-the-Trainer initiatives, facilitator readiness, and consistent training delivery across global teams. - Partner with Instructional Design to create engaging learning experiences, including instructor-led training, e-learning modules, digital learning content, videos, and knowledge resources. - Ensure all learning materials, SOPs, playbooks, assessments, and knowledge articles remain accurate, current, and aligned with operational changes. - Lead, mentor, coach, and develop a team of Learning & Development Specialists and Trainers to deliver high-quality learning experiences. - Conduct regular performance reviews, coaching sessions, and career development planning for direct reports. - Foster a culture of collaboration, accountability, innovation, and continuous learning within the Learning team. - Build succession plans and identify development opportunities to strengthen team capability. - Collaborate closely with Operations, Quality, Workforce Management, Product, and Client stakeholders to identify capability gaps and implement effective learning interventions. - Translate QA insights, CSAT/DSAT trends, operational metrics, policy updates, and product launches into targeted training initiatives. - Support new client implementations, programme launches, process transitions, and operational change management through effective learning strategies. - Participate in operational calibration sessions and leadership reviews to ensure learning priorities remain aligned with business performance. - Maintain a strong understanding of frontline operations by regularly reviewing player interactions, quality evaluations, escalation trends, and operational challenges. - Use operational insights to proactively identify knowledge gaps and continuously improve learning programs. - Analyse learning data to identify trends, risks, and opportunities for continuous improvement. - Measure and report on learning effectiveness using assessments, certification outcomes, operational performance, QA trends, productivity, and learner feedback. - Establish governance around learning quality, facilitator standards, and programme effectiveness. - Drive innovation by introducing modern learning methodologies, blended learning approaches, and scalable learning solutions. Benefits - The opportunity to join our growing business at an exciting time. - Dynamic and entrepreneurial culture. Company Description Side is a global video game development and services provider, offering technical and creative solutions to many of the largest developers and studios around the world. Founded in 2009 and drawing on 30+ years' experience from our parent company in Japan, Side has since grown to become a global force with 15+ studios in over a dozen countries across North America, Europe, South America, and Asia. Our industry-leading services include game development, art production, audio production, quality assurance, localization, localization QA, player support, community management, and datasets. Help us bring stories to the world! Join a global team of passionate gamers and contribute to delivering unforgettable game experiences.

Role Description As a Lead Machine Learning Engineer specializing in conversational and agentic systems at NobleAI, you will be responsible for architecting, building, and deploying intelligent features to our VIP platform. This role is ideal for individuals passionate about the cutting edge of LLMs and eager to build AI systems that can reason, plan, and act. - Design domain specific AI systems and chatbots capable of complex dialogue management and workflow execution via tools, API calls and multi-step tasks based on user goals, multi-agent orchestration. - Collaborate with scientists to assess, fine-tune, and deploy LLMs on domain specific data to support accuracy measurement for use cases. - Build and maintain Retrieval-Augmented-Generation (RAG) systems, Reinforcement Learning frameworks, guardrail and assessment mechanisms for end to end lifecycle for customized models. - Collaborate with product and software engineers to integrate the features into our platform. - Establish prompt engineering and data management best practices for transparency and governance. - Establish best practices for monitoring and evaluation of data and models across the model lifecycle (development, testing, and production). - Keep a pulse on the latest advancements in NLP, LLMs, and agentic AI research, and act as a subject matter expert on architecture decisions on platform and use cases. Qualifications - MSc (preferred) or BSc in Computer Science, Artificial Intelligence, or a related quantitative field. - 5+ years of hands-on experience building and deploying AI/ML systems, with a strong focus on Natural Language Processing (NLP). - Proven experience designing and shipping chatbots, virtual assistants, or agentic systems using modern LLM-based architectures. - Strong programming proficiency in Python (5+ years) and deep experience with core ML/NLP libraries such as PyTorch, TensorFlow. - Hands-on experience with LLM agent frameworks for building complex, tool-using applications, multi-agent orchestration, or establishing MCP services for platform capabilities. - Demonstrated experience with Retrieval-Augmented Generation (RAG), including the use of vector databases like Pinecone, Weaviate, or ChromaDB. - Familiarity with techniques for fine-tuning LLMs (e.g., LoRA/QLoRA) and experience working with open-source models (e.g., Llama, Mistral) or major model APIs (e.g., OpenAI, Anthropic). - 5+ years of experience with cloud platforms (Azure preferred) and familiarity with deploying AI models as scalable microservices using Docker and Kubernetes (KFP, KServe). - Solid software engineering fundamentals, including version control (Git), automated testing, and CI/CD principles. - Excellent communication skills with the ability to articulate complex technical ideas to both technical and non-technical stakeholders. Benefits - Benefits coverage including medical, dental, vision, disability, and life insurance. - Retirement fund employer contribution. - Generous Paid Time Off & Holidays. - Stock options. - Performance-based bonus. - Salary range depending on experience.

• Own small to medium components of machine learning systems from technical design through implementation and delivery • Translate technical requirements into high-quality, maintainable code and deliver workstreams according to plan • Build and maintain data pipelines and feature engineering workflows to support machine learning and AI solutions • Design, train, evaluate, and refine machine learning models with minimal supervision, applying sound statistical and engineering practices • Implement ML solutions that can be deployed into production environments as microservices, APIs, batch jobs, or streaming components • Support production monitoring efforts by helping define and implement metrics for model performance, data drift, anomalies, and retraining triggers • Collaborate with Data Engineers, Software Engineers, Data Scientists, Product partners, and business stakeholders to deliver project objectives • Understand system design, data models, and technical artifacts well enough to contribute to implementation decisions and tradeoffs • Follow governance, documentation, coding, and source control standards consistently • Demonstrate flexibility and proactively support teammates with day-to-day responsibilities as needed • Clearly document and communicate work progress, technical decisions, and outcomes to technical and non-technical audiences

Role Description Insurance isn’t the first industry most engineers think of when they imagine cutting-edge AI work. That’s exactly why this role is interesting. CFC’s Data & AI unit is building production agentic systems that automate complex underwriting decisions - not chatbots, not copilots bolted onto legacy workflows, but autonomous multi-step AI agents that reason over unstructured data, assess risk, and drive real business outcomes. We’re using frameworks like LangChain to orchestrate LLM-driven services that sit at the heart of how the business operates. The problems are genuinely hard: ambiguous inputs, high-stakes decisions, and the kind of domain complexity that makes for satisfying engineering. We’re also fundamentally rethinking how we deliver software. We’re moving toward an agentic-first development model - using AI agents not just in what we build for the business, but in how we build it. The goal is to multiply engineering delivery by an order of magnitude: not by cutting corners or generating throwaway code, but by designing robust systems and processes that let agents handle well-defined work while engineers focus on architecture, design, and the problems that actually require human judgment. This is a deliberate, engineering-led approach. We care about code quality, testability, and maintainability - the agent-generated code meets the same standards as everything else. We expect that a successful candidate will be able to bring their expertise to help guide and refine our agentic development process as it matures - a meaningful opportunity to influence how we work as well as what we ship. This is a Senior Machine Learning / AI Engineer role with genuine technical leadership scope. You’ll shape the architecture of AI-driven production microservices, own system design decisions, and work at the intersection of traditional software engineering and applied AI. You’ll collaborate closely with engineers, data scientists, and product managers in a team that’s small enough for your decisions to matter and ambitious enough for the work to stay interesting. Qualifications - Significant experience building production-grade AI agents or LLM-powered services. - Strong Python development experience, ideally with 6+ years of professional software engineering experience. - A track record of writing clean, maintainable and high-quality production code. - Experience supporting business-critical systems in live production environments. - Strong understanding of asynchronous programming, Docker, containerised deployments and modern service architectures. - Experience designing distributed systems, asynchronous microservices and event-driven architectures. - Confidence leading system design discussions and making pragmatic architectural trade-offs. - Strong cloud experience, ideally within Azure. - Experience deploying, monitoring and maintaining ML or LLM models in production. - Familiarity with MLOps, LLMOps, evals, monitoring and lifecycle management. - Hands-on experience orchestrating LLM workflows using frameworks such as LangChain. - An interest in how agentic software development can improve engineering delivery without compromising code quality. - Strong communication skills and the ability to collaborate effectively in remote or asynchronous environments. - An ownership mindset, with the ability to work independently and contribute effectively to shared codebases. Requirements - Design, develop, and maintain business-critical AI agent services. - Build tailored agent workflows and services from business requirements, using LangChain and related frameworks with reliable patterns for LLM-driven decision-making in production. - Integrate tests and validation to improve AI agents through evals and monitoring. - Work with software engineers and architects to lead system design and architectural decisions. - Translate technical specifications into clean, testable, and scalable production code. - Work closely with cross-functional teams - engineers, data scientists, product managers - to deliver features on time and to a high standard. - Write unit and integration tests to maintain reliability and service correctness. - Monitor, troubleshoot, and continuously improve production services. - Produce clear, structured documentation for systems, architecture, and processes. - Mentor junior engineers through code reviews, best-practice guidance, and knowledge sharing. Benefits - Love what you do: We show up each day ready to take on the world. Our passion and intensity set us apart and makes the difference to our colleagues, customers, brokers and carriers. - Challenge everything: We’re never afraid to question the way that things are done and we constantly challenge ourselves and others to makes things better. - Have fun, be good: Insurance is a serious business, but we don’t take ourselves too seriously. We make it fun to work at CFC, we welcome all viewpoints, and we treat everyone how we would expect to be treated.