AI / ML Ops Engineer

Zoho; United States; Remote; USD 140,000 - 210,000; Full time

Job summary

Fyerx is seeking an experienced AI/ML Ops Engineer to bridge data science research and cloud infrastructure execution. You will build automated pipelines to train, test, deploy, and monitor machine learning models and generative AI systems securely and at scale within enterprise container environments.

Key responsibilities include orchestrating containerized model deployments on Kubernetes, implementing robust model tracking with Feast and DVC, monitoring data drift and latency, and optimizing

Qualifications

• 4 to 8 years of core software engineering, DevOps, or data engineering experience, with 3+ dedicated years actively building and maintaining MLOps automation infrastructures.
• Strong technical mastery of Python programming, container orchestration (Docker, Kubernetes), ML frameworks (PyTorch, TensorFlow, Hugging Face), and advanced SQL.
• Deep understanding of distributed system mechanics, GPU resource management, deployment patterns (Shadow, Canary, A/B), and cloud provider API governance.
• Prior experience implementing Retrieval-Augmented Generation (RAG) pipelines or fine-tuning open-source LLM layers in production.
• Familiarity with infrastructure-as-code scripting tools like Terraform to provision ML cluster topologies.

Responsibilities

• Orchestrate containerized model deployments, configuring low-latency inference endpoints and auto-scaling GPU/CPU clusters on Kubernetes.
• Implement robust model tracking and data versioning foundations with Feast and DVC; manage model registries and massive datasets.
• Build automated AI performance and data monitoring gates, tracking accuracy decay, data drift, and latency in real time.
• Optimize inference environments using model compilation engines (ONNX, TensorRT) and quantization to reduce costs and improve latency.
• Integrate generative AI and LLM ops (LLMOps) with semantic caching, vector DBs (Pinecone, Milvus), and prompt validation pipelines.
• Govern ML access controls and security, establishing data separation barriers and encryption for inference logs.

Skills

Python

Kubernetes

PyTorch

TensorFlow

Hugging Face

SQL

Distributed systems

RAG

Terraform

Tools

Feast

DVC

KServe

Triton

ONNX

TensorRT

Pinecone

Milvus

Terraform

Job description

• Relevant Experience Required: 3+ years of dedicated MLOps or DevOps experience deploying and managing machine learning models in production
  
• Mandatory Certification: AWS Certified Machine Learning - Specialty, Google Cloud Certified Professional Machine Learning Engineer, or Databricks Certified Machine Learning Professional
  

Job Summary

We are seeking an experienced AI / ML Ops Engineer to bridge the gap between data science research and cloud infrastructure execution. The ideal candidate will build automated pipelines to train, test, deploy, and monitor machine learning models and generative AI systems securely and at scale within enterprise container environments.

• Orchestrate containerized model deployments, configuring low-latency inference endpoints, auto-scaling GPU/CPU clusters, and model serving runtimes on Kubernetes (KServe, Triton Inference Server).
  
• Implement robust model tracking and data versioning foundations, managing feature stores (e.g., Feast), model registries, and version controls for massive datasets using DVC.
  
• Build automated AI performance and data monitoring gates, tracking model accuracy decay, data drift indicators, concept drift parameters, and system processing latencies in real time.
  
• Optimize inference execution environments , leveraging model compilation engines (e.g., ONNX , TensorRT ) and quantization strategies to shorten response times and minimize cloud compute costs.
  
• Integrate generative AI and LLM operational frameworks (LLMOps), configuring semantic caching layers, vector database scaling parameters (e.g., Pinecone, Milvus), and prompt validation pipelines.
  
• Govern machine learning access controls and security profiles, configuring strict data separation barriers, model access tokens, and encryption protocols to safeguard sensitive inference logs.
  

Requirements

• 4 to 8 years of core software engineering, DevOps, or data engineering experience, with 3+ dedicated years actively building and maintaining MLOps automation infrastructures.
  
• Strong technical mastery of Python programming, container orchestration (Docker, Kubernetes), ML frameworks (PyTorch, TensorFlow, Hugging Face), and advanced SQL.
  
• Deep structural understanding of distributed system mechanics, GPU resource management limits, model deployment patterns (Shadow, Canary, A/B), and cloud provider API governance.
  
• Prior experience implementing RAG (Retrieval-Augmented Generation) pipelines or fine-tuning open-source LLM layers in production.
  
• Familiarity with infrastructure-as-code scripting tools like Terraform to provision ML cluster topologies.