ML Ops Engineer
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About the Role
🚀 We’re Hiring: **MLOps / LLMOps Engineer** **Location:** (Add location) **Experience:** 3\+ years **Company:** Avrioc 🔍 About the Role As part of a cross\-functional team of **engineers, data scientists, and product owners** , you’ll design, implement, and scale **MLOps \& LLMOps infrastructure** to take machine learning models from development to production— **seamlessly and efficiently** . If you’re passionate about production\-grade M
Key Skills for This Role
Full Job Posting
Overview
🚀 We’re Hiring:
Location
(Add location)
Experience
3+ years
Avrioc
🔍 About the Role
As part of a cross-functional team of
engineers, data scientists, and product owners
, you’ll design, implement, and scale
Mlops & Llmops Infrastructure
- to take machine learning models from development to production—
- seamlessly and efficiently
- .
- If you’re passionate about production-grade ML systems and cutting-edge LLM workflows,
- Avrioc is the place for you
- .
- ✅ Requirements
- Bachelor’s or Master’s degree in Computer Science, Engineering, or related field
- 3+ years of experience in
Mlops And Ml Engineering And Platform Engineering
- Hands-on experience with
Docker, Kubernetes, EKS
- Good-to-have experience with
- LLMOps best practices
- (monitoring, drift detection, prompt management, feedback loops)
- Familiarity with
- model optimization techniques
- (quantization, distillation, pruning)
- Strong experience in
- monitoring, observability, and alerting
- for ML systems
- 🛠 Technical Skills
- **Cloud:**
Aws
- **Orchestration:**
Kubernetes, EKS
- **Programming:**
Python
- **ML Frameworks:**
Pytorch And Tensorflow
- **Distributed Systems:**
Slurm, Ray
- **Hardware:**
Nvidia Dgx
- **MLOps Tools:**
Kubeflow, MLflow
- **LLM Stack:**
LangChain, Chainlit, LlamaIndex
- 🎯 Key Responsibilities
- Build and manage
- containerized ML & LLM deployments
Using Docker & Kubernetes
- Implement
- LLMOps practices
- for monitoring, drift detection, and continuous improvement
- Optimize models for
- efficient inference on AWS
- Design
- monitoring & observability systems
- for performance, latency, and resource usage
- Lead
- prompt engineering and prompt management strategies
- Collaborate with data scientists and engineers for seamless ML integration
- Automate
- model versioning and deployment
- across environments
- Design and maintain
- CI/CD pipelines for ML workflows
- Ensure
- scalable, reliable, and efficient MLOps environments
- Maintain strong
- documentation and operational standards
- Stay up to date with emerging trends in MLOps & LLMOps
- 🌟 Soft Skills
- Strong problem-solving ability
- Excellent communication and collaboration skills
- Self-driven and proactive mindset
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