MLOps Engineer

• **Job Overview**
• We are seeking a **Sr. MLOps Engineer** with 4\+ years of experience to design, automate, and manage the lifecycle of machine learning models. This role is focused on building high\-performance, scalable ML infrastructure on **Microsoft Azure** that bridges the gap between data science and production\-grade engineering. You will be responsible for creating a "Plug\-and\-Play" deployment framework that ensures our ML solutions are resilient, secure, and cost\-optimized.

Interested candidates can share their resume at reshma.dhabarde@otsi.co.in

• *Mandatory Tech stack**: Azure ML / Azure Machine Learning, GitHub Actions, MLOps / ML Ops / ML Operations, Python, SQL

Location: Bengaluru (Hybrid Model \- **Only local candidates**)

Shift: Rotational shifts(shift allowance and transportation available)

Notice period: Immediate\- 30 days

• **Key Responsibilities**

• *1\. Pipeline Architecture \& Automation**

• **Scalable ML Pipelines:** Design and manage end\-to\-end ML pipelines using **Azure ML**, **Databricks**, and **PySpark** to handle large\-scale data processing and model training.
• **DevSecOps Integration:** Build and maintain automated **CI/CD pipelines** using **GitHub Actions**, integrating **SonarQube** to enforce strict code quality and security standards.
• **Reusable Frameworks:** Develop modular templates for various ML use cases to streamline deployment and drive operational efficiency across the enterprise.

• *2\. Deployment \& Orchestration**

• **Containerization:** Utilize **Azure Kubernetes Service (AKS)** and Docker to containerize and deploy ML models, ensuring high availability and seamless scaling.
• **API Management:** Design and manage robust, secure **APIs** to facilitate seamless interactions between ML models and downstream applications.
• **Solution Architecture:** Understand and contribute to the overall system architecture to ensure ML components are modular and scalable.

• *3\. Optimization \& Governance**

• **Model Lifecycle Management:** Perform model optimization, monitor for **data drift**, and implement automated data refresh checks to maintain model accuracy.
• **Cost Engineering:** Implement cost\-monitoring strategies to ensure efficient resource utilization during high\-compute training and deployment phases.
• **Documentation:** Provide detailed technical documentation for workflows, pipeline templates, and optimization strategies to ensure long\-term maintainability.

• *4\. Collaboration**

• **Cross\-Functional Synergy:** Act as the technical liaison between Data Scientists, DevOps, and IT teams to ensure smooth model transitions across Dev, QA, and Production environments.
• **Required Qualifications**
• **Education:** Bachelor’s degree in engineering, Computer Science, or a related field.
• **Experience:** 5\+ years of total experience with a deep focus on the **Azure MLOps tool stack**.
• **Production Mastery:** Proven track record of deploying and maintaining ML models in high\-scale production environments.
• **Technical Proficiency:** \* Hands\-on expertise with **Azure Machine Learning** and **Databricks**.
• Strong understanding of **Kubernetes (AKS)** or API\-based deployment platforms.
• Solid grasp of **DevOps practices** and containerization (Docker).
• Experience with code quality automation tools like **SonarQube**.
• **Soft Skills:** Exceptional problem\-solving skills and the ability to thrive in a fast\-paced, collaborative environment.
• **Desired Qualifications**
• **Architectural Mindset:** Familiarity with broader solution architecture principles is a strong plus.

Pay: ₹1,800,000\.00 \- ₹3,300,000\.00 per year

Work Location: Hybrid remote in Bangalore City, Bengaluru, Karnataka