MLops Engineer

Straive; Bengaluru Urban; On-site; INR 1,000,000 - 1,700,000; Full time

Job summary

Straive is seeking a skilled MLOps Engineer to design, deploy, and manage production ML solutions. You will collaborate with Data Scientists, Data Engineers, and DevOps to streamline model lifecycle management and ensure reliable ML services.

The role emphasizes building scalable pipelines, automating CI/CD for ML workloads, monitoring models, and optimizing infrastructure across cloud and on‑prem environments.

Qualifications

• 5–8 years of IT experience with at least 3+ years in MLOps.
• Strong Python programming skills.
• Experience with ML platforms and deployment pipelines.
• Proficient in CI/CD for ML workflows and model management.

Responsibilities

• Design, develop, and maintain end-to-end MLOps pipelines for training, testing, deployment, and monitoring of machine learning models.
• Deploy machine learning models to cloud and on-premise environments using containerization and orchestration technologies.
• Build and automate CI/CD pipelines for machine learning applications.
• Develop reusable ML workflows for model training, validation, deployment, and versioning.
• Implement model monitoring, performance tracking, drift detection, and automated retraining strategies.
• Manage ML artifacts, datasets, feature stores, and model registries.
• Collaborate with Data Scientists to operationalize machine learning models.
• Optimize infrastructure for scalable and cost‑effective model deployment.
• Troubleshoot production issues and ensure high availability of ML services.
• Maintain security, governance, and compliance standards across ML platforms.

Skills

Python

MLOps

Git

CI/CD

REST APIs

SQL

Linux

Tools

MLflow

AWS SageMaker

Docker

Jenkins

GitHub Actions

Job description

We are looking for a highly skilled MLOps Engineer to build, deploy, automate, and manage Machine Learning solutions in production. The ideal candidate should have hands‑on experience in designing scalable ML pipelines, deploying models, automating workflows, monitoring model performance, and implementing CI/CD for machine learning applications. The role requires close collaboration with Data Scientists, Data Engineers, and DevOps teams to ensure reliable and efficient ML model lifecycle management.

Key Responsibilities

• Design, develop, and maintain end-to-end MLOps pipelines for training, testing, deployment, and monitoring of machine learning models.
• Deploy machine learning models to cloud and on-premise environments using containerization and orchestration technologies.
• Build and automate CI/CD pipelines for machine learning applications.
• Develop reusable ML workflows for model training, validation, deployment, and versioning.
• Implement model monitoring, performance tracking, drift detection, and automated retraining strategies.
• Manage ML artifacts, datasets, feature stores, and model registries.
• Collaborate with Data Scientists to operationalize machine learning models.
• Optimize infrastructure for scalable and cost‑effective model deployment.
• Troubleshoot production issues and ensure high availability of ML services.
• Maintain security, governance, and compliance standards across ML platforms.

Required Skills

• 5–8 years of IT experience with at least 3+ years of hands‑on experience in MLOps.
• Strong programming experience in Python.
• Hands‑on experience with one or more MLOps platforms:
• MLflow
• AWS SageMaker
• Experience deploying machine learning models using:
• Docker
• Strong knowledge of CI/CD tools:
• Jenkins
• GitHub Actions
• Experience with version control using Git.
• Strong understanding of machine learning lifecycle and model management.
• Experience with REST APIs for model serving.
• Knowledge of SQL and data engineering concepts.
• Experience with Linux and shell scripting.

Experience with at least one of the following:

• Amazon Web Services (AWS)

Good to Have

• Experience with Apache Airflow or Prefect for workflow orchestration.
• Knowledge of Terraform or Infrastructure as Code (IaC).
• Experience with Spark or PySpark for large‑scale data processing.
• Familiarity with Databricks.
• Experience with Kafka or other streaming platforms.
• Knowledge of Feature Store implementation.
• Experience with monitoring tools such as Prometheus, Grafana, ELK, or Azure Monitor.
• Exposure to LLMOps, Generative AI, or Large Language Models is an added advantage.