AI Engineer (Production AI / ML Ops Focus)

About the Role

We are looking for an AI Engineer who focuses on operating and maintaining AI features in production environments. This role emphasizes ensuring that AI/LLM applications are reliable, scalable, and continuously improving, rather than only building prototypes.

You will work closely with product and engineering teams to ensure AI systems are stable, measurable, and ready for real-world usage.

Responsibilities

• Maintain and operate AI/LLM features in production, including monitoring, troubleshooting, and performance optimization

• Ensure system reliability by handling incident response, rollback strategies, and root cause analysis

• Improve system performance through latency, throughput, and cost optimization, with measurable outcomes

• Build and maintain reproducible deployment pipelines, including containerization, configuration management, and version control

• Establish and improve evaluation and testing frameworks for AI features, including datasets, test cases, and regression checks

• Collaborate with cross-functional teams to translate AI capabilities into stable and production-ready systems

Requirements

• Solid experience in Python and backend/system development

• Hands-on experience deploying and maintaining AI/ML/LLM applications in production environments

• Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes)

• Experience in system monitoring, logging, and debugging

• Strong understanding of performance tuning and system reliability

Nice to Have

• Experience with LLM applications, such as RAG systems or AI-powered features

• Familiarity with evaluation frameworks, observability tools, or experiment tracking

• Knowledge of data pipelines and workflow orchestration tools

• Experience with model optimization or lightweight fine-tuning (e.g., LoRA, PEFT)

What We Value

• A strong sense of ownership in maintaining production-quality systems

• Ability to balance delivery speed and system stability

• Clear communication of trade-offs and technical decisions