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

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

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

AI Engineer