ML Ops Engineer

Tekskills; Bengaluru; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Tekskills in Bengaluru is seeking an experienced ML Ops Engineer to build end-to-end ML pipelines using Azure and Palantir Foundry. The role requires 6+ years of experience and hands-on expertise in Python, Spark, SQL, and cloud data platforms.

You will design scalable data pipelines for feature engineering, model training, and deployment, and work with data scientists and MLOps teams to productionize AI/ML workflows.

Qualifications

• 6+ years of experience in ML/AI or data engineering.
• Hands-on experience with cloud data platforms and modern ML tooling.
• Strong programming in Python and data querying skills.

Responsibilities

• Build and maintain end-to-end ML pipelines including training, validation, model registry, and deployment using Azure and Palantir Foundry.
• Design and develop 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 closely with data scientists and MLOps teams to convert prototypes into robust, production-ready AI/ML solutions.

Skills

Python

Spark

SQL

Cloud data platforms

Azure

AI/ML pipelines knowledge

Data APIs

Tools

Palantir Foundry

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.