MLOp's Engineer

About Es Magico

Es Magico is an AI-first enterprise transformation organisation that goes beyond consulting — we deliver scalable execution across sectors such as BFSI, Healthcare, Entertainment, and Education. With offices in Mumbai and Bengaluru, our mission is to augment the human workforce by deploying bespoke AI employees across business functions, innovating swiftly and executing with trust. We also partner with early-stage startups as a venture builder, transforming 0 → 1 ideas into AI-native, scalable products.

Role: MLOps Engineer

  Location: Bengaluru (Hybrid)

  Experience: 1–4 years

  Joining: Immediate

About the Role

We are seeking a skilled MLOps Engineer with 3+ years of hands-on experience in managing complex machine learning pipelines and deploying machine learning models at scale. You will be responsible for the entire ML lifecycle, from data ingestion to model deployment, monitoring, and optimization. This role requires a strong foundation in Python, Databricks, MLflow, and cloud platforms, with a deep focus on the operationalization of machine learning models.

Key Responsibilities

Pipeline Management: Design, develop, and maintain complex ML pipelines using tools like MLflow, Databricks, Apache Airflow, Azure Machine Learning, and Azure Databricks for scalable ML operations.

Data Ingestion and Management: Develop and maintain efficient data ingestion pipelines, ensuring the seamless integration of structured and unstructured data from various sources into the ML workflow using Azure Data Factory, Azure Databricks, Apache Kafka, Apache Nifi, and Azure Synapse. Implement ETL processes to prepare data for training, validation, and testing, and integrate data lakes like Azure Data Lake Storage and Azure Blob Storage to store and manage large-scale datasets.

Model Deployment & Monitoring: Deploy machine learning models into production environments, ensuring seamless model versioning, automated retraining, and ongoing performance monitoring using Azure Kubernetes Service (AKS), Azure DevOps, Azure Monitor, and Grafana.

Collaboration: Work closely with data scientists and software engineers to transition models from research into production, with a focus on automating deployment pipelines using tools like GitLab CI/CD, Jenkins, and Azure DevOps.

Infrastructure as Code: Utilize Infrastructure as Code (IaC) tools such as Terraform, Ansible, and CloudFormation for managing ML environments and automating cloud infrastructure provisioning.

Model Governance: Implement and manage model versioning, testing, and retraining procedures using Git and MLflow, ensuring continuous improvement of model accuracy and performance.

Automation & Optimization: Automate routine tasks such as data preprocessing, model training, and hyperparameter tuning with tools like Kubeflow, Azure Machine Learning, and Hyperopt to ensure efficient use of resources.

Performance Monitoring: Set up metrics and dashboards for real-time performance tracking and alerting for model inference accuracy, drift, and latency using Prometheus, Grafana, Azure Monitor, and Splunk.

Cloud Integration: Leverage Azure cloud platforms for scalable infrastructure, focusing on storage, compute, and orchestration services such as Azure Blob Storage, Azure Synapse, and Azure Data Lake.

CI/CD for ML: Implement Continuous Integration and Continuous Deployment (CI/CD) pipelines specifically for ML workflows using Azure DevOps, Jenkins, and GitLab, ensuring reliable, repeatable, and automated model deployments.

Key Skills and Requirements:

Experience: Minimum 3 years of hands-on experience in MLOps with a strong focus on complex pipeline management and production-level ML systems.

Azure Experience: Mandatory hands-on experience with Azure cloud platforms, specifically Azure Machine Learning and related services for deployment and orchestration.

Programming: Strong experience with Python and ML frameworks like TensorFlow, PyTorch, Scikit-learn, and others.

Tools & Technologies:

MLOps Tools: Databricks, MLflow, Kubeflow, TensorFlow Extended (TFX), and Apache Airflow.

Cloud Platforms: Azure (mandatory hands-on experience with Azure Machine Learning and related services for deployment and orchestration).

Containerization: Proficiency in Docker and Kubernetes for containerized ML model deployment.

Version Control: Familiarity with Git, GitLab CI/CD, and other version control tools.

Data & Model Monitoring: Experience with Prometheus, Grafana, and custom monitoring solutions for ML models.

CI/CD Pipelines: Experience in designing and automating CI/CD pipelines tailored for machine learning models, integrating them with cloud platforms and model management tools.

Data Engineering: Solid understanding of ETL processes, data pipelines, and integration with data lakes or warehouses, including Azure Data Lake, Azure Blob Storage, and Azure Synapse.

Model Deployment: Hands-on experience with deployment strategies (e.g., A/B testing, canary deployments) and model rollback procedures.

Communication & Collaboration: Strong communication skills with the ability to work collaboratively across multi-functional teams.

Preferred Qualifications:

Education: Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field.

Certifications: Microsoft Certified: Azure AI Engineer Associate, Microsoft Certified: Azure Data Scientist Associate, or other relevant Azure certifications.

Agile Methodologies: Experience working in Agile/Scrum environments.

What We Offer:

Opportunity to work in a cutting-edge environment with top-tier machine learning technologies.

A collaborative team atmosphere where your contributions directly influence the company's growth.

Competitive salary and benefits package.

How to Apply

 Send your resume to careers@esmagico.in with the subject line: "Application – MLOps Engineer".