Machine Learning Engineer

• Design, build, and deploy machine learning models and GenAI solutions for real business problems
• Develop end-to-end ML pipelines covering data ingestion, feature engineering, training, evaluation, deployment, and retraining
• Build and productionize GenAI applications involving LLM integration, prompt engineering, RAG pipelines, embeddings, vector databases, and agentic workflows
• Fine-tune, evaluate, and optimize classical ML, deep learning, and LLM models for accuracy, latency, and cost
• Deploy and operate models in production on AWS or comparable cloud platforms
• Implement MLOps practices including experiment tracking, model versioning, CI/CD, automated testing, and monitoring
• Design data pipelines, ensure data quality, and build feature stores with data engineering
• Establish evaluation frameworks for traditional and LLM-based systems
• Embed fairness, explainability, privacy, and security into model development and deployment
• Collaborate with product managers, architects, and client stakeholders to translate business requirements into measurable ML solutions
• Write clean, tested, production-quality code and participate in design and code reviews
• Build proofs of concept and harden successful experiments into production systems
• Mentor junior engineers and data scientists
• Stay current with ML/GenAI developments and recommend valuable models, frameworks, and techniques

Requirements

• Minimum 5 years of software or data engineering experience, with at least 3 years building and deploying machine learning models in production
• Strong programming skills in Python
• Hands-on experience with scikit-learn, PyTorch, or TensorFlow
• Practical experience building GenAI/LLM applications, including prompt engineering, RAG pipelines, embeddings, vector databases, and LLM APIs
• Solid grounding in supervised and unsupervised learning, feature engineering, model evaluation, and error analysis
• Experience deploying and operating models on AWS or comparable cloud platforms, including SageMaker, Bedrock, Lambda, ECS/EKS, or equivalent services
• Working knowledge of MLOps tooling, experiment tracking, model registries, pipeline orchestration, and monitoring
• Strong data skills, including SQL, Apache Airflow, relational and NoSQL data stores
• Experience exposing models as REST/GraphQL APIs, batch and real-time inference, and backend integration
• Software engineering fundamentals including version control, testing, CI/CD, Docker/Kubernetes, and code review practices
• Understanding of responsible AI concepts including bias, explainability, privacy, and security
• Experience working in Agile/SCRUM environments and delivering iteratively
• Excellent communication skills for explaining models, trade-offs, and results to technical and non-technical audiences
• Ability to work with stakeholders across multiple geographies
• Must already be eligible to work in the Republic of Ireland

Core Competencies

Demonstrates expertise in designing and deploying machine learning models and GenAI solutions, with a strong focus on MLOps practices and cloud deployment on AWS. Proficient in building end-to-end ML pipelines and ensuring data quality while embedding responsible AI principles.

Highest-signal resume keywords

• Machine Learning Model Development
• GenAI Application Development
• AWS Deployment
• MLOps Practices
• Python Programming

ATS Optimization Keywords Hard Skills

• Machine Learning
• GenAI
• Python
• Scikit-learn
• PyTorch
• TensorFlow
• SQL
• Docker
• Kubernetes
• Feature Engineering

Soft Skills

• Excellent Communication
• Collaboration
• Mentoring

Industry Keywords

• MLOps
• Agile
• Supervised Learning
• Unsupervised Learning
• Data Engineering

Tools & Technologies

• AWS SageMaker
• AWS Lambda
• Apache Airflow
• REST APIs
• GraphQL APIs