Match Score
Against your primary resumeLogin to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Protocol Intelligence
Data-driven signals on your job's competitivenessRemote role but niche MLOps and mandatory certifications reduce applicant pool.
MLOps and LLMOps specialization with cloud ML certifications makes background fit highly domain-specific.
Mandatory cloud ML certification plus explicit MLOps and 4–8 years experience creates strict filters.
Job Description
Structured overview of role & requirementsAbout This Role
Design and automate end-to-end ML pipelines for training, deployment, and monitoring using tools like Kubeflow, MLflow, or AWS SageMaker Pipelines.
Orchestrate containerized AI/ML model deployments on Kubernetes, including configuring inference endpoints and auto-scaling GPU/CPU clusters.
Implement model tracking, data versioning, AI performance monitoring, model security, and integrate LLM operational frameworks for generative AI systems.
Minimum Requirements
4 to 8 years total software engineering, DevOps, or data engineering experience with 3+ years dedicated MLOps experience.
Mandatory certification: AWS Certified Machine Learning Specialty, Google Cloud Certified Professional ML Engineer, or Databricks Certified Machine Learning Professional.
Strong technical skills in Python, container orchestration (Docker, Kubernetes), ML frameworks (PyTorch, TensorFlow, Hugging Face), and advanced SQL.
Work Mode: Remote (Offshore location).
Ideal Candidate Profile
Experienced in building scalable MLOps infrastructures with deep knowledge of distributed systems and GPU resource management.
Familiar with production patterns like shadow, canary, and A/B model deployments and cloud provider API governance.
Has hands-on experience integrating generative AI/LLM ops including semantic caching, vector databases, and prompt validation pipelines.