Machine Learning Engineer

5 days ago

Anywhere in IndiaMultiple Locations Cyanous Software Full time ₹ 15,00,000 - ₹ 25,00,000 per year

Job Description :

We are seeking a highly skilled Machine Learning Engineer with expertise in building and deploying end-to-end ML solutions. The ideal candidate will have strong experience in model development, deployment, and monitoring in cloud environments (preferably Azure). You will be responsible for the full ML lifecycle, ensuring robust, scalable, and production-ready ML systems.

Key Responsibilities :

- Design and implement end-to-end ML pipelines: data ingestion - preprocessing - model training - evaluation - deployment - monitoring.

- Deploy ML models into production using Azure ML endpoints, Docker, FastAPI, or Flask.

- Build and manage CI/CD pipelines for ML workflows using Azure DevOps, GitHub Actions, or Jenkins.

- Implement monitoring and logging frameworks (Azure Monitor, Application Insights, or custom logging) for deployed ML models.

- Optimize ML models for performance, scalability, and cost efficiency.

- Collaborate with data scientists, data engineers, and DevOps teams to integrate ML solutions into business applications.

- Stay updated on emerging ML frameworks, tools, and best practices.

Required Skills & Qualifications :

years of experience as a Machine Learning Engineer or similar role.

- Strong programming skills in Python and libraries such as scikit-learn, TensorFlow, or PyTorch.

- Hands-on experience with Azure ML and cloud-based model deployment.

- Proficiency in Docker and microservice-based deployment using FastAPI/Flask.

- Solid understanding of MLOps principles and CI/CD tools (Azure DevOps, GitHub Actions, Jenkins).

- Experience in monitoring & logging frameworks (Azure Monitor, Application Insights, Prometheus, or similar).

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

Preferred Qualifications :

- Experience with big data tools (Spark, Databricks).

- Familiarity with feature stores and data versioning tools (MLflow, DVC).

- Knowledge of model explainability and bias detection techniques.

- Exposure to Agile methodology and cross-functional collaboration.

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