AI Engineer

Education and skills requirements

· Bachelor's or Master's degree in Data Science, Computer Science, Mathematics, Statistics, or a related field

· Advanced proficiency in Python programming with a focus on writing clean, testable and efficient code

· DevOps & Containers: Proficient with Docker for containerization and working knowledge of Kubernetes (k8s) for orchestration

· Practical understanding of GPU architecture and cloud compute instances to optimize resource allocation for training and inference workloads

· MLOPS tools: hands on experience with MLflow (or similar tools like weights & biases) for experiment tracking and model registry

· Proven experience working with Large Language Models (LLMs)

· Good understanding of AI agents & agentic workflows, LLM orchestration frameworks and reasoning patterns

· Experience with data preprocessing, feature engineering, and model selection and evaluation techniques

· Hands-on experience with CI/CD pipelines (GitLab, Jenkins)

· Knowledge of statistical and mathematical concepts relevant to machine learning, such as probability, linear algebra, and optimization

· Excellent problem-solving and debugging skills, with the ability to identify and resolve issues quickly and effectively

· Relevant work experience in machine learning, data science or a related field.

GENERAL DESCRIPTION

We are seeking a skilled machine learning platform engineer (MLOps) to join our agile platform team which is part of our ML & AI ART. In this role, Bridge the gap between experimental data science and production-grade systems. You'll contribute across the entire lifecycle - from concept to deployment - and collaborate closely with cross-functional teams to deliver high-quality digital solutions. Further, you drive the orchestration of advanced agentic workflows to enable autonomous, AI-driven systems. You will be responsible for engineering robust data pipelines, establishing comprehensive model management lifecycles, overseeing all foundational platform-level AI integrations – including engineering a robust library of AI skills for agent use.

kEY FEATURES OF THE POSITION

Functional / Technical

· Design, develop and deploy machine learning solutions and services

· Implement end-to-end machine learning pipelines from data ingestion to training and model serving

· Operationalize LLMs, embeddings, and multi-agent systems in real-world applications

· Manage the machine learning and model lifecycle (experimentation, registry, deployment)

· Oversee the model promotion lifecycle, coordinating validation gates and approval workflows to safely deploy new model versions from stating to production

· Containerize applications using Docker and orchestrate them via Kubernetes

· Build and maintain CI/CD pipelines for ML models and LLM applications

· Collaborate with data scientists to refactor research code into production-ready Python code

· Monitor model performance, data drift, and performance in production

· Assess and integrate AI solutions ensuring optimal performance and reliability

· Design and implement production grade RAG systems

· Collaborate with infrastructure teams, data engineers, data scientists, and other stakeholders to integrate machine learning solutions into existing systems and processes

· Participate in code reviews, testing, and debugging to ensure the quality and reliability of machine learning solutions

SKILLS REQUIREMENTS OF THE POSITION:

Competencies

· Strong problem-solving and analytical skills, with the ability to think critically and creatively about complex challenges

· Excellent communication and collaboration skills, with the ability to work effectively with cross-functional teams and stakeholders at all levels of the organization

· Ability to manage personal workloads effectively, to prioritize tasks, manage timelines, and deliver high-quality results on schedule

· Continuous learning mindset, with a passion for staying up to date with the latest advancements in machine learning and artificial intelligence

· Attention to detail and commitment to producing high-quality, reliable, and maintainable code