Modelops Engineer

3 - 6 years

18 - 22 Lacs

Posted:6 hours ago| Platform: Naukri logo

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Job Type

Full Time

Job Description

Modelops Engineer JD:

Location: Bengaluru Or Willing to Relocate to Bengaluru

Key Requirements:

We are looking for a skilled and detail-oriented MLOps Engineer with 3+ years of experience to join our ML engineering team. The ideal candidate will be responsible for ensuring seamless model deployment, monitoring, scalability, and performance tuning in production environments. You should be hands-on with Python, Databricks, mlFlow, Seldon, and experienced in cloud-based deployments (AWS).

You will also contribute to prompt engineering and LLM app development using Langchain, and help productionize recommendation algorithms and other ML applications.

Key Responsibilities:

  1. Design, develop, and maintain CI/CD pipelines for AI, ML models.
  2. Collaborate with Data Scientists to build scalable model deployment architectures.
  3. Automate data and model validation, retraining, and monitoring pipelines.
  4. Ensure reliability, security, and scalability of ML infrastructure.
  5. Monitor model performance and data drift in production environments.
  6. Integrate ML models with APIs, web services, and production systems.
  7. Implement best practices for version control and reproducibility of ML workflows.
  8. Manage containerized ML applications using Docker and Kubernetes.

Qualifications:

  1. Bachelors or Masters degree in Computer Science, Engineering, or a related field.
  2. Proven experience of at least 3 years in MLOps, DevOps, or ML Engineering roles.
  3. Proficiency in Python, Bash, and scripting languages.
  4. Hands-on experience with ML platforms (e.g., MLflow, Kubeflow, SageMaker).
  5. Familiarity with cloud platforms (preferred AWS)
  6. Experience with CI/CD tools (e.g., Jenkins, GitHub Actions).
  7. Strong understanding of ML model lifecycle and deployment challenges.
  8. Excellent problem-solving and communication skills

Preferred Skills:

  1. Experience with infrastructure-as-code tools (e.g., Terraform).
  2. Knowledge of data engineering and ETL pipelines.
  3. Exposure to monitoring tools like Prometheus, Grafana.

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