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.