MLOps Engineer
The MLOps Engineer (frequently titled LLMOps or AI Platform Engineer) builds dependable infrastructure for training, versioning, deploying, and monitoring machine learning and generative models in production.
The MLOps Engineer (frequently titled LLMOps or AI Platform Engineer) builds dependable infrastructure for training, versioning, deploying, and monitoring machine learning and generative models in production.
Key responsibilities include CI/CD automation for ML pipelines, container orchestration (Docker, Kubernetes), GPU utilization and inference optimization (such as Triton), and real-time monitoring to detect data drift and quality regressions.
Methodologies & technical foundations (% penetration)
Infrastructure & tooling demanded in job specs (% penetration)
Seniority Level Breakdown
Corporate hierarchy distribution
Top Hiring Enterprises
Leading corporations posting this role
- United Parcel Service2 positions
- Skydance Corporation2 positions
- UnitedHealth Group2 positions
- TE Connectivity2 positions
- Expand Energy1 positions
- Gen Digital1 positions
S&P 500 Sector Distribution
Industry sectors generating the highest hiring demand
- Industrials28.6%
- Information Technology21.4%
- Health Care14.3%
- Communication Services14.3%
- Energy14.3%
Live Enterprise Job Postings
Sample of active postings directly from corporate ATS boards
Senior AI MLOps/DevOps/Cloud Architect
Machine Learning Operations Engineer I
Senior MLOps Engineer
Senior Machine Learning Operations Engineer, Agentic Systems Platform Engineering
Senior – MLE – Python, ML Frameworks, MLOps, Containerization, Terraform, GCP, Vertex AI, IBM Watsonx
Advisor IT Systems - AI/ML Ops
MLOps Engineer - MoneyLion
Oklahoma City Sr_ Ml Ops Engineer
Recommended Skill Tracks & Certifications
Curated programs on Coursera, DataCamp and cloud providers aligned with these role requirements
Google Cloud Professional Data Engineer Specialization
Official curriculum by Google Cloud covering large-scale data processing with BigQuery, streaming pipelines with Dataflow, and modern lakehouse architecture.
Data Engineer with Python Career Track
Interactive, browser-based coding track covering robust data pipelines, PostgreSQL, unit testing, software design patterns for data, and PySpark fundamentals.
Generative AI with Large Language Models
Created by Andrew Ng and AWS: practical engineering foundations of transformer architectures, pre-training, instruction fine-tuning, and alignment evaluation in production.
Google Cloud Professional Data Engineer
Gold standard certification assessing design, security, scalability, and operational reliability of BigQuery and GCP analytical pipelines.
Databricks Certified Data Engineer Professional
Validates mastery of streaming, batch ETL, Unity Catalog governance, and production Lakehouse architectures with Delta Lake and Spark.