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8th Element Digital Private Limited logo

ML Ops Specialist

5 years

2 - 8 Lacs

Hyderābād

Posted:3 weeks ago| Platform: GlassDoor logo

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Skills Required

ml strategy design integration learning model deployment azure reliability pipeline devops automate training testing collaboration data strategies optimization tuning management governance security programming flask fastapi containerization orchestration gitlab github kubernetes service development communication consulting drift monitoring agile

Work Mode

On-site

Job Type

Part Time

Job Description

Key Responsibilities

ML Ops Strategy & Implementation: Design, implement, and maintain end-to-end MLOps pipelines, ensuring seamless integration of machine learning models into production environments.
Model Deployment & Monitoring: Utilize Azure ML services to deploy models efficiently and monitor their performance, ensuring reliability and scalability.
CI/CD Pipeline Development: Develop and manage continuous integration and continuous deployment pipelines using Azure DevOps or similar tools to automate model training, testing, and deployment processes.
Collaboration & Consultation: Work closely with data scientists, engineers, and business stakeholders to understand requirements and translate them into robust MLOps solutions.
Performance Optimization: Implement strategies for model optimization, including hyperparameter tuning and resource management, to enhance model accuracy and efficiency.
Governance & Compliance: Ensure that deployed models adhere to organizational policies, security standards, and regulatory requirements.

Required Skills & Qualifications

Experience: Minimum of 5 years in machine learning roles, with at least 2–3 years focused on MLOps, specifically in deploying and managing models in production.
Technical Proficiency:
Strong programming skills in Python, including frameworks like Flask, FastAPI, and libraries such as Pandas and NumPy.
Hands-on experience with Azure Machine Learning, including model training, deployment, and monitoring.
Familiarity with containerization technologies like Docker and orchestration tools such as Kubernetes.
Experience with CI/CD tools like Azure DevOps, GitLab CI, or GitHub Actions.
Knowledge of MLflow, Azure Databricks, and Azure Kubernetes Service (AKS).
Portfolio: Demonstrated experience with at least 2–3 production-level implementations of machine learning models, showcasing the ability to transition models from development to production environments effectively.
Soft Skills: Excellent communication and consulting skills, with the ability to collaborate across teams and present complex technical concepts to non-technical stakeholders.

Preferred Qualifications

Experience with model governance, drift detection, and performance monitoring in production settings.
Familiarity with Azure governance tools, cost management, and policy enforcement.
Exposure to Agile methodologies and project management tools like Azure Boards or JIRA.

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