AI Engineer

Location : Dublin, hybrid Type of employment : Permanent As an AI Engineer, you will be a key member of our AI delivery team, responsible for designing, building, and scaling AI microservices that transform proof-of concept (PoC) notebooks into robust, production-ready components. Working closely with data scientists, MLOps engineers, and cross-functional partners, you will enable the seamless integration of AI services into the broader client ecosystem. This role offers a unique opportunity to bridge engineering and AI best practices in Applied AI. Key Responsibilities * Domain-specific model training: build reusable pipelines that leverage different post-training techniques to train models for specific domains. * AI Microservice Development: Design and implement AI-powered microservices (e.g., ML, agentic AI) with a focus on modularity, scalability, and reusability to support diverse business units and solutions. * Productionalization:Transition AI models and agentic workflows from notebook based PoCs to production-grade components, ensuring performance, reliability, and maintainability. * API Development: Design and implement robust APIs for AI microservices to facilitate seamless integration within the client ecosystem. * Quality Assurance: Develop and execute rigorous testing strategies (unit, integration, performance) to ensure the accuracy, stability, and quality of deployed AI services. * Performance Optimization: Identify and address bottlenecks in AI microservices and infrastructure, optimizing for latency, throughput, and cost efficiency. * Pipeline Development: Work with data science and infrastructure teams to design reusable workflows (e.g., feature engineering, model optimization, evaluation pipelines). * Cross-Functional Collaboration: Partner with data scientists, MLOps engineers, product owners, and external stakeholders to translate business requirements into technical specifications. * Technology Innovation: Research and evaluate emerging technologies (e.g., LLMs, frameworks, methodologies) to enhance AI software development and large-scale data processing capabilities. * Compliance & Ethics: Ensure all AI microservices adhere to client’s security standards, compliance policies, and ethical AI principles, while contributing to AI engineering best practices. Qualifications * Education: Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field. Master’s degree preferred. * Experience: LLM post-training experience is mandatory and highly preferred. Minimum of 7+ years in software development, with at least 3 years focused on building and deploying AI/ML powered applications or microservices in production environments. Technical Skills * Extensive experience in model post training techniques (e.g., SFT, Distillation, Pruning and reinforcement learning). * Strong proficiency in Python, Java and relevant frameworks (e.g., Spring Boot, Spring AI, FastAPI, LangChain, LangGraph, Semantic Kernel). * Extensive experience designing, developing, and deploying RESTful APIs and microservices. * Proficiency with containerization technologies. * Proven experience delivering GenAI, Agentic AI workflows in production. * Proven experience scaling machine learning models from prototype to production, including familiarity with feature stores, model registries, and inference patterns. * Solid understanding of the AI/ML lifecycle, from data preparation and model training to deployment and monitoring. * Experience with cloud platforms and their relevant compute, storage, and AI/ML services, cloud certification preferred. * Good understanding of ML, Deep Learning. * Good understanding of neural network and transformer model architecture. * Good experience of CI/CD pipelines for automated testing and deployment of software and AI models. * Understanding of data governance, data quality, and data security principles relevant to AI/ML applications. * Excellent communication, interpersonal, and stakeholder management skills, with the ability to effectively articulate complex technical concepts to both technical and non technical audiences.