ML Ops Engineer

Tekskills; Bulandshahr; On-site; INR 1,500,000 - 2,300,000; Full time

Job summary

Tekskills is seeking an experienced ML Ops Engineer to design, develop and deploy end-to-end ML pipelines. You will work across Azure and Palantir Foundry platforms, building scalable data pipelines for feature engineering, model training, and real-time or batch inference.

Ideal candidates have 6+ years of experience in ML engineering or MLOps, with strong Python, Spark, SQL skills and cloud technologies. The role is based in Bangalore/Hyderabad with on-site collaboration and production-grade ML

Qualifications

• 6+ years of experience in ML engineering or MLOps.
• Rel 4+ Years indicated in posting.

Responsibilities

• Build and maintain end-to-end ML pipelines including training, validation, model registry, and deployment on Azure/Foundry.
• Design scalable data pipelines for feature engineering and model training with Python, Spark, SQL, and cloud tech.
• Operationalize AI/ML workflows by integrating models with data APIs and production-grade practices.
• Optimize model performance and data quality via experiments, validation, monitoring, and automated tests.
• Collaborate with data scientists and MLOps to turn 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

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.