Model deployment engineer

About the Role

Model Deployment Engineer

Key Responsibilities

• Deploy machine learning models into production environments, ensuring scalability, reliability, and performance.
  
• Collaborate with data scientists, ML engineers, and software developers to understand model requirements and system constraints.
  
• Design and implement

  APIs, microservices, or containerized applications

  for model serving.
• Optimize model inference for low latency, high throughput, and cost efficiency.
  
• Monitor deployed models for

  performance, drift, and anomalies

  , implementing retraining pipelines as needed.
• Automate deployment workflows using

  CI/CD pipelines and DevOps practices

  .
• Integrate models with existing software systems, data pipelines, and cloud platforms.
  
• Ensure compliance with

  security, data privacy, and regulatory standards

  in deployed models.
• Document deployment processes, architecture, and best practices for knowledge sharing.
  

Required Skills & Qualifications

• Bachelors or Master’s degree in

  Computer Science, Data Science, Software Engineering, or related field

  .
• 2–6 years of experience in

  ML/AI model deployment, MLOps, or production engineering

  .
• Proficiency in

  Python, Java, or similar programming languages

  .
• Experience with

  model serving frameworks

  like TensorFlow Serving, TorchServe, MLflow, or BentoML.
• Hands-on experience with

  containerization (Docker) and orchestration (Kubernetes)

  .
• Familiarity with

  cloud platforms

  (AWS, Azure, GCP) and their ML services.
• Knowledge of

  CI/CD pipelines, version control, and DevOps practices

  .
• Understanding of

  machine learning concepts, model evaluation, and performance metrics

  .
• Strong problem-solving, debugging, and communication skills.
  

Nice-to-Have

• Experience with

  real-time model serving and streaming data inference

  .
• Knowledge of

  feature stores, data pipelines, and ETL frameworks

  .
• Familiarity with

  A/B testing, model rollback strategies, and canary deployments

  .
• Exposure to

  ML monitoring tools

  (Prometheus, Grafana, Evidently AI, WhyLabs).
• Experience in

  scalable model optimization

  (quantization, pruning, acceleration).