AI Engineer

beatmysalary; Dublin; On-site; EUR 100,000 - 140,000; Full time

Job summary

Beatmysalary is seeking an experienced AI Engineer to design, build, and scale AI microservices that convert PoCs into robust production components. You will collaborate with data scientists, MLOps, and cross-functional teams to embed AI services into client ecosystems and drive practical AI engineering best practices.

Ideal candidates have a strong background in post-training techniques, Python/Java, and cloud-native architectures, with a focus on scalable, governance-aware AI solutions across

Qualifications

• LLM post-training experience is mandatory.
• Minimum of 7+ years in software development, with 3+ years in AI/ML powered apps or microservices in production environments.

Responsibilities

• Domain-specific model training: build reusable pipelines for domain-specific models.
• AI Microservice Development: design and implement AI-powered microservices for scalability and reusability.
• Productionalization: transition PoCs to production-grade AI components with reliability.
• API Development: design robust APIs for AI microservices for client ecosystems.
• Quality Assurance: develop and execute testing strategies for AI services.
• Performance Optimization: identify bottlenecks and optimize latency/cost in AI microservices.
• Pipeline Development: design reusable ML workflows with data science and infra teams.
• Cross-Functional Collaboration: work with data scientists, MLOps, product owners, and stakeholders.
• Technology Innovation: research emerging AI tech to enhance AI software and data processing.
• Compliance & Ethics: ensure AI services adhere to security, compliance, and ethical standards.

Skills

Python

Java

AI/ML lifecycle

CI/CD

Containerization

Communication

Cloud platforms

Education

Bachelor’s degree in Computer Science or related field

Master’s degree preferred

Tools

Spring Boot

Spring AI

FastAPI

LangChain

LangGraph

Semantic Kernel

Job description

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.