AI Engineer

We are seeking a skilled machine learning platform engineer (MLOps) to join our agile platform team. In this role, 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 will 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.

Job Responsibilities

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

Job Requirements

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

Skills requirements

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

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