AI / ML Ops Engineer
FyerX (company site, opens in a new tab)5–8 yrsRemoteDirect career pageEst. 12–28 LPA
Posted on 23 Sept
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Job description
Structured overview of role & requirements
About 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.