We are looking for a MLOps Engineer with strong production engineering experience building and operating scalable ML and AI systems.
This is a software-first MLOps platform role focused on production reliability, ML lifecycle management, large-scale training infrastructure, operational AI systems, and reusable platform capabilities.
You will help build and scale the production platform behind our Industrial AI Workforce, enabling teams across the company to develop, evaluate, deploy, and operate ML and AI systems consistently and safely.
A Day In Your Life
Design and evolve production MLOps capabilities across the full ML lifecycle including datasets, features, models, evaluations, deployments, monitoring, retraining, and feedback signals.
Build systems for experiment tracking, artifact management, reproducibility, versioning, lineage, promotion workflows, and production readiness.
Develop reusable platform tooling, golden paths, and engineering standards that improve consistency and delivery velocity across teams.
Build operational infrastructure for LLM and agentic systems including prompts, tools, traces, evaluations, observability, safety boundaries, and production monitoring.
Design evaluation and monitoring frameworks for AI systems including answer quality, latency, grounding, reliability, and operational regressions.
Build and optimize large-scale training pipelines supporting heterogeneous data sources and scalable compute patterns.
Write clean, modular, production-grade Python services and platform libraries.
Drive engineering quality through automated testing, CI/CD, observability, deployment standards, and operational best practices.
Requirements: What You Bring
5+ years of professional software engineering, MLOps, or ML platform engineering experience in production environments.
Significant experience building or owning production ML infrastructure and lifecycle systems.
Strong Python engineering skills with production-grade architecture, modular design, testing, packaging, and robust error handling.
Strong understanding of the end-to-end ML lifecycle including training, deployment, monitoring, retraining, reproducibility, and lineage.
Experience working with large-scale data platforms such as Databricks, Spark, Delta Lake, or equivalent ecosystems.
Experience with ML platform and MLOps frameworks such as MLflow, Metaflow, Kubeflow, or equivalent ML lifecycle-management systems.
Proven ability to design reusable workflow orchestration using Airflow, Metaflow, or Databricks, covering automation, scheduling, dependency management, and production reliability.
Familiarity with operational patterns for LLMOps, AgentOps, and production AI systems.
Strong written and verbal communication skills in English.
Nice to Have
Experience with industrial, IoT or manufacturing platforms.
Experience with feature stores, model registries, dataset versioning, and lineage systems.
Experience with AI agents, RAG systems, production GenAI applications, or evaluation frameworks.
This position is open to all candidates.