AI/ML Ops Engineer

FyerX - Your Trusted Marketing Partner

. Design and automate end-to-end ML pipelines for continuous training and continuous deployment using Kubeflow, MLflow, or AWS SageMaker Pipelines .

Posted 9/23/2026contractRemote • IndiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and automating end-to-end ML pipelines, with a strong focus on MLOps automation infrastructures and model deployment strategies. Proficient in leveraging cloud technologies and container orchestration for optimized AI performance and security.

Highest-signal resume keywords

MLOps Automation InfrastructurePython ProgrammingAWS Certified Machine Learning - SpecialtyKubernetes Container OrchestrationModel Deployment Patterns

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning PipelinesModel TrackingData VersioningSQL ProficiencyDistributed SystemsGPU Resource ManagementRAG PipelinesFine-Tuning LLM LayersQuantization StrategiesFeature Stores

Tools & Technologies

KubeflowMLflowAWS SageMakerDockerKServeTriton Inference ServerONNXTensorRTHugging FaceTerraform

Certifications & Qualifications

AWS Certified Machine Learning - SpecialtyGoogle Cloud Certified Professional Machine Learning EngineerDatabricks Certified Machine Learning Professional

Industry Keywords

Continuous TrainingContinuous DeploymentLow-Latency InferenceAuto-Scaling ClustersData DriftConcept DriftModel Accuracy DecayCloud Provider API GovernanceSemantic CachingVector Database Scaling

Tech Stack

Tools & technologies

AWSCloudDockerKubernetesPythonPyTorchSQLTensorflowTerraform

About the role

Key responsibilities & impact

Design and automate end-to-end ML pipelines for continuous training and continuous deployment using Kubeflow, MLflow, or AWS SageMaker Pipelines
Orchestrate containerized model deployments on Kubernetes using KServe and Triton Inference Server
Configure low-latency inference endpoints and auto-scaling GPU/CPU clusters
Implement model tracking and data versioning foundations, including feature stores, model registries, and DVC
Build automated AI performance and data monitoring gates for model accuracy decay, data drift, concept drift, and processing latencies
Optimize inference environments using ONNX, TensorRT, and quantization strategies
Integrate generative AI and LLM operational frameworks with semantic caching, vector database scaling, and prompt validation pipelines
Govern machine learning access controls and security profiles, including data separation, model access tokens, and encryption protocols

Requirements

What you’ll need

4 to 8 years of core software engineering, DevOps, or data engineering experience
3+ dedicated years actively building and maintaining MLOps automation infrastructures
AWS Certified Machine Learning - Specialty, Google Cloud Certified Professional Machine Learning Engineer, or Databricks Certified Machine Learning Professional certification required
Strong technical mastery of Python programming
Experience with Docker and Kubernetes container orchestration
Experience with PyTorch, TensorFlow, and Hugging Face
Advanced SQL proficiency
Deep understanding of distributed system mechanics
Understanding of GPU resource management limits
Understanding of Shadow, Canary, and A/B model deployment patterns
Understanding of cloud provider API governance
Prior experience implementing RAG pipelines or fine-tuning open-source LLM layers in production preferred
Familiarity with Terraform preferred

Benefits

Comp & perks

Remote work arrangement
Contract employment