Experience: 4-15Years

Location: Pan India(Metro Cities)

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.

1. 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.

1. 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.

1. 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.

1. Model Retraining & Continuous Improvement:

- Automate and manage model retraining processes based on incoming current data or changing business needs.
- Create frameworks for evaluating and improving model accuracy, efficiency, and robustness.

1. 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.

1. 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 model’s runtime efficiency.

1. 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.

1. 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:

Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field.
1. 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.

1. 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.

1. 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.

1. 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.

📌 MLOPS Engineer (Bengaluru)
🏢 Tata Consultancy Services
📍 Bengaluru