MLOps Engineer (Industrial AI)

TL;DR

MLOps Engineer (Industrial AI): Building and operating scalable ML and AI systems for industrial machine health with an accent on production reliability, ML lifecycle management, and large-scale training infrastructure. Focus on developing reusable platform tooling, operational infrastructure for LLM/agentic systems, and optimizing high-scale data pipelines.

Company

hirify.global provides manufacturers and industrial sectors with AI-driven insights into the health of machines and processes to optimize production outcomes.

What you will do

• Design and evolve production MLOps capabilities across the full ML lifecycle, including datasets, models, deployments, and monitoring.
• Build systems for experiment tracking, artifact management, versioning, and production readiness.
• Develop reusable platform tooling, golden paths, and engineering standards to improve delivery velocity.
• Construct operational infrastructure for LLM and agentic systems, including prompts, traces, observability, and safety boundaries.
• Design evaluation and monitoring frameworks for AI answer quality, latency, and operational regressions.
• Optimize large-scale training pipelines supporting heterogeneous data sources and scalable compute patterns.

Requirements

• 5+ years of professional software engineering, MLOps, or ML platform engineering experience in production environments.
• Strong Python engineering skills with expertise in production-grade architecture and modular design.
• Deep understanding of the end-to-end ML lifecycle, including training, deployment, and reproducibility.
• Experience with large-scale data platforms such as Databricks, Spark, or Delta Lake.
• Proficiency with MLOps frameworks (MLflow, Metaflow, Kubeflow) and workflow orchestration (Airflow).
• Strong written and verbal communication skills in English.

Nice to have

• Experience with industrial, IoT, or manufacturing platforms.
• Knowledge of feature stores, model registries, and dataset versioning.
• Experience with AI agents, RAG systems, or GenAI evaluation frameworks.

Culture & Benefits

• People-first organization fostering an inclusive environment where diverse perspectives are encouraged.
• Commitment to a workplace free of discrimination and harassment.
• Culture of authenticity and leveraging individual strengths.
• Equal opportunity employer.