Client: HTC Global Services Location: Bangalore / Chennai / Hyderabad, India Experience: 6 – 10 Years Salary: ₹19,00,000 – ₹25,00,000 per annum Work Mode: Hybrid (2nd Shift) Joining Timeline: Immediate to 15 days Interview Process: 3 Rounds
Job Summary
We are seeking a skilled MLOps Engineer to design, deploy, and manage scalable machine learning systems in production environments. The ideal candidate will have strong experience in ML engineering, DevOps, and data engineering, with a focus on building reliable pipelines, automating workflows, and ensuring model performance and stability.
Key Responsibilities
Model Deployment
Deploy machine learning models into production using scalable frameworks
Build APIs and batch/real-time data pipelines
Containerize ML models using Docker
Orchestrate workflows and deployments using Kubernetes
CI/CD for Machine Learning
Design and implement CI/CD pipelines for ML workflows
Automate model training, validation, testing, and deployment
Implement version control for code, datasets, and models
Monitoring & Maintenance
Monitor model performance, accuracy, and drift
Ensure system reliability, scalability, and performance
Troubleshoot production issues and optimize pipelines
Required Skills & Qualifications
Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field
6+ years of experience in ML Engineering, DevOps, or Data Engineering
Proven experience deploying ML models in production environments
Strong understanding of microservices architecture
5+ years of hands-on experience in ML, MLOps, DevOps, and Python (mandatory)
Technical Skills
Programming:
Python (mandatory)
Familiarity with Java or Scala (preferred)
ML Frameworks:
TensorFlow, PyTorch, Scikit-learn
MLOps Tools:
MLflow, Kubeflow, or SageMaker
Airflow or Prefect
Cloud Platforms:
AWS, Azure, or GCP
Containerization & Orchestration:
Docker, Kubernetes
CI/CD Tools:
Jenkins, GitHub Actions, or GitLab CI
Data Technologies (Preferred):
SQL, Spark, Kafka
Must-Haves
6+ years of relevant experience in ML Engineering / DevOps / Data Engineering
Strong expertise in Python, ML, MLOps, and DevOps practices
Experience deploying and managing ML models in production
Understanding of microservices architecture
Ability to join within 15 days
Nice-to-Have Skills
Experience with SQL, Apache Spark, and Kafka
Exposure to large-scale distributed data systems
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