AI Engineer

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

SKILLS REQUIREMENTS OF THEPOSITION

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