AI / ML Ops Engineer

This is a remote position.

• Employment Type: Contract
  
• Work Mode: Remote
  
• Location: Offshore
  
• Total Experience Required: 4 to 8 years
  
• 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
  

• Design and automate end-to-end ML pipelines (Continuous Training and Continuous Deployment) using orchestration engines like Kubeflow, MLflow, or AWS SageMaker Pipelines.
  
• 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.
  
• Mandatory certification: AWS Machine Learning Specialty, Google Cloud Professional ML Engineer, or Databricks ML Professional.
  

• 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.