# Engineer / Sr. Engineer - Data & MLOps

> Weekday AI · Bengaluru, India · Full-time · Posted 2026-09-15

**Salary:** INR 1,400,000–3,200,000

**Workplace:** on_site

**Department:** Weekday's Client via platform

## Description

𝗧𝗵𝗶𝘀 𝗿𝗼𝗹𝗲 𝗶𝘀 𝗳𝗼𝗿 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝗲𝗸𝗱𝗮𝘆'𝘀 𝗰𝗹𝗶𝗲𝗻𝘁𝘀

𝗦𝗮𝗹𝗮𝗿𝘆 𝗿𝗮𝗻𝗴𝗲: 𝗥𝘀 𝟭𝟰𝟬𝟬𝟬𝟬𝟬 - 𝗥𝘀 𝟯𝟮𝟬𝟬𝟬𝟬𝟬 (𝗶𝗲 𝗜𝗡𝗥 𝟭𝟰-𝟯𝟮 𝗟𝗣𝗔)

Experience: 2+ yrs

Location: Bengaluru, Karnataka, India

Job Type: Full-time

We are looking for a technically strong **DataOps / MLOps Engineer** to build, deploy, operate, and scale modern data, machine learning, and Generative AI infrastructure in enterprise cloud environments.

The role focuses on creating reliable and automated engineering pipelines across **DataOps, MLOps, LLMOps, and AI platforms**, with strong emphasis on **Databricks and AWS**. The ideal candidate will have hands-on experience with cloud data platforms, CI/CD automation, model deployment, observability, infrastructure management, and production operations.

## Requirements

### KEY RESPONSIBILITIES

-   Design, build, and maintain scalable **DataOps, MLOps, and LLMOps pipelines** for data, ML, and Generative AI workloads.
-   Automate data provisioning, model deployment, model evaluation, monitoring, and enterprise workflow execution.
-   Implement and maintain robust **CI/CD pipelines** for data pipelines, ML models, containerised services, and AI applications.
-   Manage, configure, optimise, and scale **Databricks workspaces, clusters, jobs, and enterprise data-processing environments**.
-   Collaborate with Data Engineers, ML Engineers, Software Engineers, and Product teams to deploy data pipelines, feature stores, ML models, and serving systems into production.
-   Implement proactive monitoring, event instrumentation, alerting, and self-healing mechanisms for data and model quality issues.
-   Support incident response, production troubleshooting, infrastructure upgrades, capacity planning, and cloud resource optimisation.
-   Work with DevOps, SRE, IT, and Security teams to implement governance, data lineage, compliance, security, and enterprise deployment standards.
-   Monitor and troubleshoot distributed data pipelines, ETL workflows, model-serving systems, and production ML infrastructure.
-   Build and maintain observability and model-monitoring capabilities using tools such as **MLflow, Weights & Biases, and LangSmith**.
-   Develop hands-on **Proofs of Concept (POCs)** for modern data platforms, feature stores, streaming technologies, and ML infrastructure.
-   Implement infrastructure provisioning and configuration management using tools such as **Terraform, CloudFormation, or Ansible**.
-   Support containerised applications and ML workloads using **Docker and Kubernetes**.
-   Participate in code reviews, engineering design discussions, on-call support, and knowledge-sharing initiatives.
-   Identify opportunities to improve reliability, scalability, automation, cost efficiency, and engineering productivity.
-   Stay current with evolving DataOps, MLOps, LLMOps, cloud, AI, and data-platform technologies.
-   Contribute to the continuous modernisation of enterprise data and ML infrastructure.

### WHAT MAKES YOU A GREAT FIT

-   **2–5 years of experience** across **DataOps, MLOps, ML Engineering, or Data Engineering** in enterprise cloud environments.
-   Strong hands-on expertise in **Databricks administration, workspace management, cluster optimisation, jobs, and enterprise data workloads**.
-   Strong experience with **AWS data and ML services**, including services such as SageMaker, Glue, EMR, Athena, and S3.
-   Solid understanding of **DevOps, DataOps, MLOps, and LLMOps** methodologies and practices.
-   Proven experience building **CI/CD automation pipelines** for containerised Python, Java, or Scala applications, microservices, and ML-serving systems.
-   Strong understanding of model lifecycle management, evaluation, monitoring, governance, and observability.
-   Experience with tools such as **MLflow, Weights & Biases, LangSmith**, or similar platforms.
-   Strong understanding of databases, replication, relational and NoSQL databases, and **vector databases** such as Pinecone, FAISS, Milvus, or Weaviate.
-   Practical experience deploying, monitoring, debugging, and supporting distributed data pipelines and ETL workflows.
-   Strong Git knowledge and familiarity with standard branching and collaborative development workflows.
-   Experience with **Terraform, CloudFormation, Ansible**, or similar infrastructure-as-code and configuration-management tools.
-   Proficiency in at least one scripting/programming language such as **Python, Bash, or JavaScript**.
-   Hands-on experience with **Docker** and exposure to **Kubernetes** orchestration.
-   Good understanding of the machine learning lifecycle, feature engineering, and ML deployment workflows.
-   Familiarity with **PyTorch, TensorFlow, NLP, computer vision, and Generative AI** concepts is desirable.
-   Strong troubleshooting, analytical, and problem-solving skills.
-   Excellent cross-functional communication and collaboration skills.
-   Demonstrated ability to promote a collaborative **DevOps/DataOps/MLOps culture**.
-   Strong ownership mindset with the ability to support production systems and participate in on-call responsibilities.
-   Ability to adapt quickly to evolving technology stacks and contribute to modernisation initiatives.

## Apply

[Apply at Weekday AI](https://apply.workable.com/weekday-1/j/88BCED6FE6/apply)

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