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MLOps Engineer at Weekday AI
Bengaluru, karnataka, India -
Full Time

Start Date

Immediate

Expiry Date

23 Dec, 25

Salary

0.0

Posted On

24 Sep, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

MLOps, Python, PySpark, T-SQL, Azure Databricks, CI/CD, GitHub Actions, FastAPI

Industry

technology;Information and Internet

Description
This role is for one of the Weekday's clients Min Experience: 3 years Location: Bangalore JobType: full-time We are seeking an experienced MLOps Engineer to design, orchestrate, and manage end-to-end machine learning pipelines on modern cloud platforms. This role requires strong expertise in MLOps practices, backend development, and cloud-based deployment, ensuring scalable, reliable, and production-ready ML solutions. Key Responsibilities Build and manage ML pipelines across the full lifecycle: data/feature engineering, model training/inference, and real-time/batch processing. Work extensively with Azure and Databricks platforms to enable scalable ML solutions. Develop backend services and APIs using FastAPI to support ML workflows. Implement MLOps best practices, including monitoring data drift, model drift, and online learning. Collaborate with data engineers, data scientists, and cross-functional stakeholders to deliver business-focused ML solutions. Use Python, PySpark, and T-SQL for development and data orchestration. Set up and maintain CI/CD workflows with GitHub Actions for continuous integration and deployment. (Good to have) Contribute to deployment and monitoring of LLM-based GenAI solutions. Qualifications 3–5 years of relevant experience as an MLOps Engineer. Strong hands-on expertise in MLOps, Python, PySpark, Azure Databricks, and CI/CD pipelines. Knowledge of backend frameworks (FastAPI) and modern cloud-based ML deployments. Strong problem-solving, debugging, and collaborative skills. Skills Core: MLOps, Python, PySpark, T-SQL, Azure Databricks, CI/CD (GitHub Actions), FastAPI Preferred: LLM-based GenAI deployment
Responsibilities
Build and manage ML pipelines across the full lifecycle, including data/feature engineering, model training/inference, and real-time/batch processing. Collaborate with data engineers, data scientists, and cross-functional stakeholders to deliver business-focused ML solutions.