Walk-in || Mlops Developer

Job description

Experience: 4-15Years

Location: Kolkata

Job Title: ML Ops Engineer

Responsibilities:

1. Model Deployment & Integration:

- Design, develop, and manage automated pipelines for deploying machine learning models into production.

- Ensure smooth integration between model development, data, and application teams.

- Implement model versioning and rollback strategies to facilitate easy model updates and troubleshooting.

2. Infrastructure Automation:

- Build and maintain scalable infrastructure using tools like Kubernetes, Docker, and cloud platforms (AWS, Azure, GCP).

- Automate the deployment process and manage model serving environments.

- Design and optimize cloud-native solutions to ensure scalability and performance under heavy workloads.

3. Monitoring and Maintenance:

- Continuously monitor the performance and health of deployed models in production environments.

- Implement real-time logging, alerting, and monitoring systems to ensure models effectiveness over time.

- Detect, troubleshoot, and resolve issues such as model drift, degradation, and inefficiencies.

4. Collaboration with Data Scientists & DevOps:

- Work closely with data scientists to ensure that models are production-ready and meet system requirements.

- Collaborate with DevOps teams to integrate MLOps tools and practices into the CI/CD pipeline.

- Optimize model performance by coordinating with various teams to manage the lifecycle of machine learning models.

5. Model Retraining & Continuous Improvement:

- Automate and manage model retraining processes based on incoming new data or changing business needs.

- Create frameworks for evaluating and improving model accuracy, efficiency, and robustness.

6. Security & Compliance:

- Ensure the security of machine learning systems, including data protection, model access control, and sensitive data handling.

- Ensure compliance with relevant regulatory requirements related to data privacy and security.

7. Performance Optimization:

- Work on optimizing models and system performance for faster inference and low-latency predictions.

- Implement techniques like quantization, pruning, and model distillation to optimize the models runtime efficiency.

8. Documentation and Reporting:

- Maintain comprehensive documentation for model deployment pipelines, monitoring setups, and operational procedures.

- Provide regular reports on system performance, model health, and operational metrics to stakeholders.

9. Research & Development:

- Stay up-to-date with emerging MLOps technologies and best practices.

- Research and implement new tools and frameworks to improve operational efficiency

Skills and Qualifications:

1. Educational Background:

Bachelors or Masters degree in Computer Science, Engineering, Mathematics, or a related field.

2. Technical Skills:

- Strong experience with cloud platforms (AWS, Google Cloud, Azure).

- Familiarity with Docker, Kubernetes, and containerization technologies

- Proficiency in programming languages such as Python, Java, or Go

- Experience with CI/CD tools (Jenkins, GitLab CI, etc.) and automation frameworks.

- Familiarity with version control systems (e.g., Git).

- Working knowledge of machine learning frameworks (TensorFlow, PyTorch, Scikit-learn, etc.).

- Experience with model serving tools like TensorFlow Serving, TorchServe, or MLFlow.

3. Data Management Skills:
- Strong knowledge of data pipelines and ETL processes.
- Experience with Big Data technologies (Spark, Hadoop, Kafka) is a plus.
- Expertise in data preprocessing and **feature engineering.

4. Monitoring & Logging Tools:
- Proficiency with monitoring tools like Prometheus, Grafana, or Datadog.
- Experience with logging frameworks such as ELK stack (Elasticsearch, Logstash, Kibana) or Splunk.

5. Software Engineering & System Design:
- Strong understanding of **software engineering principles** and best practices.
- Experience in designing highly available, fault-tolerant, and scalable distributed systems.
- Familiarity with **DevOps practices** and agile methodologies.