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Ml Ops

2 weeks ago

Pune Maharashtra, India Princeton IT America Full time

**Job Title: Senior MLOps Engineer**

**Experience**: 5.5+ Years
**Location**: Pune
**Employment Type**: Full-Time

**Looking for immediate joiners**

**Role Overview**:
**Key Responsibilities**:

- Design, build, and manage scalable **ML pipelines** for training, deployment, and monitoring.
- Collaborate with data scientists and engineers to **operationalize ML models** into production.
- Implement **CI/CD workflows** for ML lifecycle management.
- Optimize ML models for **performance, scalability, and cost efficiency**.
- Develop and maintain **model monitoring, logging, and alerting systems**.
- Work on **cloud-native platforms (AWS, GCP, Azure)** for ML deployment and orchestration.
- Ensure **data security, compliance, and governance** within ML operations.
- Research and integrate **MLOps best practices** and tools into existing workflows.

**Required Skills & Experience**:

- **5.5+ years** of experience in **Machine Learning Engineering / MLOps / Data Engineering**.
- Strong experience with **Python, SQL, and ML frameworks** (TensorFlow, PyTorch, Scikit-learn).
- Hands-on expertise in **CI/CD pipelines** (Jenkins, GitHub Actions, GitLab CI/CD, etc.).
- Proficiency in **containerization and orchestration** (Docker, Kubernetes, Kubeflow, MLflow, Airflow).
- Solid knowledge of **cloud services** (AWS SageMaker, GCP Vertex AI, Azure ML).
- Experience with **monitoring tools** (Prometheus, Grafana, ELK Stack).
- Strong understanding of **version control** (Git), **model registry**, and **artifact management**.
- Knowledge of **data pipelines** and **ETL processes**.
- Excellent problem-solving, debugging, and communication skills.

**Good to Have**:

- Exposure to **feature stores** and data cataloging tools.
- Experience with **A/B testing frameworks** and ML model performance evaluation.
- Knowledge of **MLOps security best practices**.
- Certification in **Cloud / ML Engineering**.

Pay: ₹1,800,000.00 - ₹2,000,000.00 per year

**Experience**:

- MLops: 6 years (preferred)

Work Location: In person