MLOps Engineer

2 weeks ago


Noida, Uttar Pradesh, India FCS Software Solutions Full time US$ 1,50,000 - US$ 2,00,000 per year

Job Title: MLOps Engineer

Work Timing: US EST Hours

Duration: Contract

Experience: 8+ years

Location: India (Remote)

Job Description:

We are seeking an experienced Machine Learning Operations (MLOps) Engineer to join our AI & Machine Learning Platform Team. The ideal candidate will have strong expertise in cloud infrastructure, CI/CD, container orchestration, and ML model deployment. You will work at the intersection of Data Science, Data Engineering, and DevOps, enabling scalable ML solutions and production-grade AI applications.

Key Responsibilities:

  • Design and deploy scalable ML infrastructure using cloud and containerization technologies (Azure, Kubernetes, AKS, Docker).
  • Collaborate with cross-functional teams to build cloud-hosted, automated pipelines for running, monitoring, and retraining ML models.
  • Implement model and pipeline validation procedures with Data Scientists, Data Engineers, and ML Engineers.
  • Optimize and refactor development code to ensure seamless production deployments.
  • Build and maintain data and feature engineering pipelines to support ML models.
  • Define configurations and specifications for automated production environments.
  • Develop CI/CD pipelines to enable continuous integration, testing, and deployment of ML models and features.
  • Support AI/ML applications and MLOps practices for production-grade deployments.

Required Skills:

  • Strong experience with:
  • Azure Cloud
  • Azure DevOps (CI/CD pipelines)
  • Kubernetes & AKS
  • Databricks (Data & ML platform)
  • Solid understanding of machine learning model deployment into production.
  • Hands-on experience with platform engineering and MLOps practices.

Nice-to-Have Skills:

  • Experience with ML Infrastructure deployments.
  • Exposure to AI applications and advanced MLOps frameworks.

Thanks & Regards:

Kanika Katiyar

Associate Recruiter

FCS Software Solutions Limited


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