This is a remote position.

AI / ML Ops Engineer

Job Details

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

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.

Key Responsibilities

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.

Preferred Qualifications

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.

Kỹ năng yêu cầu

Python programmingDocker containerizationKubernetes orchestrationML pipeline orchestration (Kubeflow, MLflow, AWS SageMaker Pipelines)Model serving runtimes (KServe, Triton Inference Server)Feature stores and data/version control (Feast, DVC, model registries)Model monitoring and drift detection (data drift, concept drift, performance gates)Model optimization and compilation (ONNX, TensorRT) and quantization strategiesLLMOps and vector databases (semantic caching, Pinecone, Milvus)ML frameworks (PyTorch, TensorFlow, Hugging Face)Advanced SQLGPU resource management and distributed systems designMachine learning security and access controls (encryption, tokens, data separation)Infrastructure-as-code (Terraform)Cloud ML certification (AWS Certified ML Specialty / GCP Professional ML Engineer / Databricks ML Professional)

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