MLOPS Engineer

Tata Consultancy Services; Kolkata District; On-site; INR 900,000 - 1,400,000; Full time

Job summary

Tata Consultancy Services in Kolkata is seeking a seasoned MLOps Engineer to design and manage automated pipelines for deploying machine learning models into production. You will ensure seamless collaboration between data science, data engineering and application teams, implement versioning/rollback, and build scalable cloud-native infrastructure.

Role emphasizes collaboration with DevOps, setting up CI/CD for ML and maintaining monitoring, security, and performance optimization across models

Qualifications

• Bachelor's or Master's degree in CS, Engineering, Mathematics or related field.
• Strong experience with cloud platforms (AWS, Google Cloud, Azure).
• Familiarity with Docker, Kubernetes, and containerization technologies.
• Proficiency in Python, Java, or Go.
• Experience with CI/CD tools (Jenkins, GitLab CI) and automation frameworks.
• Familiarity with Git version control.
• Working knowledge of ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Experience with model serving tools (TensorFlow Serving, TorchServe, MLFlow).
• Strong knowledge of data pipelines and ETL processes.
• Experience with monitoring and logging tools (Prometheus, Grafana, ELK).
• Understanding of DevOps practices and scalable distributed systems.

Responsibilities

• 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 for easy updates and troubleshooting.
• Build and maintain scalable infrastructure using Kubernetes, Docker, and cloud platforms (AWS, Azure, GCP).
• Automate deployment processes and manage model serving environments.
• Design cloud-native solutions for scalability and performance under heavy workloads.
• Monitor performance and health of deployed models in production.
• Implement real-time logging, alerting, and monitoring for model health.
• Detect, troubleshoot, and resolve issues like drift and degradation.
• Collaborate with data scientists and DevOps to production-readiness and CI/CD integration.
• Coordinate model retraining processes based on new data and changing business needs.
• Create frameworks for evaluating and improving model accuracy and robustness.
• Ensure security and compliance for ML systems and data handling.
• Optimize model runtime with quantization, pruning, and distillation.
• Maintain documentation for deployment pipelines and monitoring setups.
• Provide regular reports on system performance and operational metrics.

Skills

Python

Java

Go

CI/CD

DevOps

ML Ops

Education

Bachelor's or Master's in CS/Engineering/Math

Tools

Docker

Kubernetes

Spark

Hadoop

Kafka

TensorFlow

PyTorch

MLFlow

Prometheus

Grafana

ELK Stack

Job description

- 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.

- 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.

- 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.