AI Engineer

An AI Engineer designs, builds, and deploys scalable AI/ML systems that enable data-driven decision-making and automation across the organization. They bridge the gap between data science experimentation and production, ensuring models are efficient, reliable, and integrated into business systems. The role focuses on operationalizing machine learning and generative AI solutions with strong performance, security, and governance standards.

Responsibilities

• Design and develop end-to-end AI solutions, from model training pipelines to deployment environments, ensuring scalability, performance, and reliability.
• Build APIs, microservices, and integration layers to embed AI models within business systems, data platforms, or digital products.
• Design and operate MLOps frameworks for model lifecycle management including CI/CD for ML, automated testing, and deployment orchestration.
• Establish model governance controls, ensuring proper versioning, documentation, and rollback procedures.
• Collaborate with infrastructure and DevOps teams to ensure secure, compliant, and cost-optimized environments for AI workloads.
• Work closely with data platform teams to integrate feature stores, vector databases, and data lakes for AI model consumption.
• Integrate enterprise data sources with LLMs using retrieval-augmented generation (RAG) techniques and embeddings.
• Support AI use case delivery squads by designing reusable components, shared pipelines, and standardized deployment frameworks.

Requirements

• Bachelor’s or master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, or a related field.
• 4–8 years of professional experience in software or data engineering, with at least 3 years focused on AI/ML engineering or MLOps.
• Demonstrated experience delivering production-grade AI systems, integrating models into business applications, or managing enterprise-scale ML infrastructure.
• Hands-on experience in building applications using modern AI frameworks (TensorFlow, PyTorch, scikit-learn) and supporting tooling (MLflow, Kubeflow, Airflow).
• Experience with inference optimization techniques (quantization, model compression, distillation) and serving platforms.
• Familiarity with DevOps principles, infrastructure as code (Terraform, Helm), and security standards for AI environments are beneficial.

What We Offer
At the company, we’re on a mission to create great moments, to spread happiness, to build experiences that stay in our memories for a lifetime. We’re proud to say that over the past 27 years, we have built a reputation as a regional market leader in what we do. Join us and work in a friendly environment, where everyone shares positive vibes and is excited about our future.