ML Ops Engineer

Tekskills; Hyderabad; On-site; INR 1,800,000 - 2,600,000; Full time

Job summary

Tekskills in India seeks an experienced Engineer (ML Ops Engineer) to design, build and operate end-to-end ML pipelines in cloud platforms. You will collaborate with data scientists and MLOps teams to deploy scalable AI/ML solutions across real-time or batch inference.

The role requires hands-on experience with Python, Spark and SQL, cloud data platforms, and governance of model registries and deployment pipelines on Azure and Palantir Foundry.

Qualifications

• 6+ years of experience in ML Ops or related field.
• Hands-on experience with end-to-end ML pipelines in cloud environments.
• Proficiency in Python, Spark, and SQL for data processing and model deployment.
• Experience with cloud platforms and data platforms including Azure and Palantir Foundry.

Responsibilities

• Build and maintain end-to-end ML pipelines including training, validation, model registry, and deployment using Azure and Palantir Foundry.
• Design scalable data pipelines for feature engineering, model training, and real-time or batch inference using Python, Spark, SQL, and cloud technologies.
• Operationalize AI/ML workflows by integrating models with data APIs and production-grade ML engineering practices.
• Optimize model performance and data quality through experimentation, validation, monitoring, and automated testing.
• Collaborate with data scientists and MLOps teams to convert prototypes into production-ready AI/ML solutions.

Skills

Python

Spark

SQL

Cloud data platforms

Azure

Palantir Foundry

Data APIs

AI/ML pipelines knowledge

Tools

Palantir Foundry

Azure

Data APIs

Job description

• Job Title: Engineer (ML Ops Engineer)

Work Location: Bangalore / Hyderabad

Experience Required: 6+ years (Rel 4+ Years)

Skills Required

• Python, Spark, SQL
• Cloud data platforms
• Azure
• Palantir Foundry
• Data APIs
• AI/ML pipelines knowledge

Roles & Responsibilities

1. Build and maintain end-to-end ML pipelines including training, validation, model registry, and deployment using platforms such as Azure and Palantir Foundry.
2. Design and develop scalable data pipelines for feature engineering, model training, and real-time or batch inference using Python, Spark, SQL, and cloud technologies.
3. Operationalize AI/ML workflows by integrating models with data APIs and production-grade ML engineering practices.
4. Optimize model performance and data quality through experimentation, validation, monitoring, and automated testing.
5. Collaborate closely with data scientists and MLOps teams to convert prototypes into robust, production-ready AI/ML solutions.