Join

MLOps Engineer

LockedIn AI, Remote, US

Responsibilities

LockedIn AI is hiring a hands-on MLOps Engineer to own the full machine learning lifecycle—from model training to production deployment, monitoring, and continuous optimization. 

You will bridge the gap between AI development and production engineering, ensuring that all models powering our real-time AI copilot run reliably and efficiently for over 1 million users. 

Key Responsibilities 

  • ML Lifecycle & Deployment 
  • Own end-to-end deployment of ML models including LLMs, RAG systems, and speech models 
  • Build and maintain model registries, versioning, and artifact tracking systems 
  • Implement safe deployment strategies like canary releases, A/B testing, and rollback mechanisms 

ML Pipeline Automation 

  • Design and maintain CI/CD pipelines for ML workflows 
  • Automate training, validation, testing, and deployment processes 
  • Build retraining pipelines triggered by drift, performance drops, or schedules 
  • Manage ML orchestration tools such as MLflow, Kubeflow, Airflow, or Prefect 

Monitoring & Model Performance Build observability systems for real-time model performance tracking Detect model drift, data drift, and performance degradation Create dashboards for latency, accuracy, cost, and usage metrics Implement alerting systems with clear remediation workflows 

Infrastructure & Cost Optimization 

  • Manage GPU-based compute infrastructure for training and inference 
  • Optimize scaling strategies to balance performance and cost 
  • Containerize ML workloads using Docker and Kubernetes 
  • Monitor and reduce costs across compute, storage, and LLM usage 

Data & Feature Pipelines 

  • Build automated data pipelines for training and inference 
  • Ensure data quality, validation, and reproducibility 
  • Work with feature stores and data engineering teams 
  • Maintain data lineage and version control for all ML assets 

Security & Governance 

  • Enforce secure handling of ML data and model artifacts 
  • Implement access control, encryption, and audit logging 
  • Ensure compliance with privacy and security standards 
  • Monitor risks like model extraction and adversarial attacks

Qualifications

Required Qualifications 

  • 3+ years of experience in MLOps, ML engineering, or DevOps with ML systems 
  • Experience building end-to-end ML pipelines in production 
  • Strong knowledge of Python and ML frameworks (PyTorch, TensorFlow, etc.) 
  • Hands-on experience with Docker, Kubernetes, and cloud platforms (AWS/GCP/Azure) 
  • Familiarity with CI/CD for ML systems and model monitoring tools 
  • Experience working with data scientists and AI engineers 

Preferred Qualifications 

  • Experience with LLM deployment, RAG systems, or real-time AI inference 
  • Background in distributed training or GPU cluster management 
  • Familiarity with ML governance and model lifecycle management 
  • Experience with tools like MLflow, Arize AI, or WhyLabs 
  • Contributions to MLOps or AI infrastructure projects

Compensation

Compensation: $140,000 – $200,000 USD / year

How to apply

Submit: Resume/CV Short note explaining why you want to join LockedIn AI Optional GitHub, portfolio, or technical writing.

Apply