Walk-in | Mlops Developer

Tata Consultancy Services; Kolkata District; On-site; INR 1,500,000 - 2,600,000; Full time

Job summary

Tata Consultancy Services is seeking an ML Ops Engineer in Kolkata to design and manage automated pipelines for production ML deployments. You will drive integration with data science and DevOps, implement versioning, and ensure scalable, secure model serving.

You will work with cloud platforms (AWS, GCP, Azure), containers, and CI/CD tools, focusing on monitoring, performance, and continuous improvement of ML systems in production.

Qualifications

• Bachelor's or Master's in CS/Engineering, Mathematics, or related field.
• Strong cloud, containerization, and orchestration knowledge.
• Proficiency with Python, Java, or Go for automation and tooling.
• Experience with CI/CD and Git-based workflows.
• Familiarity with ML frameworks and model serving tools.

Responsibilities

• Design, develop, and manage automated ML model deployment pipelines.
• Ensure smooth integration between model development, data, and app teams.
• Implement model versioning and rollback for easy updates.
• Build and maintain scalable infrastructure using Kubernetes, Docker, and cloud platforms.
• Automate deployment processes and manage model serving environments.

Skills

Cloud platforms

Docker & Kubernetes

Programming languages

CI/CD tools

Git

ML frameworks

Model serving

Data pipelines

DevOps practices

Education

CS/Engineering degree

Tools

Python/Java/Go

Job description

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.