MLOps Engineer | Hybrid | Atlanta, GA | SaaS | $150K - $200K
A technology company is embedding AI into its core product workflows - predictive models, intelligent automation, and LLM-enabled features - and needs an engineer who can build and own these systems in production. This is a hands-on role at the intersection of applied ML and platform engineering. You'll be setting the standard for how AI is built and operated here.
What you'll do
- Design, build, and deploy production ML models - REST inference services, batch pipelines, real-time scoring
- Establish MLOps practices: model versioning, monitoring, alerting, retraining, and lifecycle governance
- Evaluate new AI use cases and select the right approach - supervised learning, embeddings, retrieval-driven architectures, or hybrid
- Integrate ML outputs into product workflows alongside application engineering teams
- Partner with Data Engineering to ensure AI-ready data pipelines and structures
- Work with external AI partners initially, then progressively take ownership in-house
What you need
- 7+ years in ML engineering or applied ML, with production-grade systems in enterprise environments
- Strong Python and ML library experience - scikit-learn, PySpark, pandas
- Solid MLOps background: MLflow or equivalent model lifecycle tooling
- Cloud-native experience - Azure preferred; AWS or GCP considered
- Pragmatic approach to modelling - picks the right tool for the problem, not the most fashionable one
- Experience with ML and backend development tools: Python, MLflow / MLOps, Azure, scikit-learn / PySpark, REST, inference, APIs, Model monitoring, LLMs
Nice to have
- Databricks - for scalable training pipelines and unified data + ML workflows
- Vector search, embeddings, or retrieval-augmented generation (RAG) experience
- Hospitality, travel, or similarly data-rich consumer industry background
