Experience: 4-15Years

Location: Kolkata

Job Title: ML Ops Engineer

Responsibilities

- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Research & Development:
- Stay up-to-date with emerging MLOps technologies and best practices.
- Research and implement recent tools and frameworks to improve operational efficiency

Skills and Qualifications

- Educational Background:

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

📌 Walk-in || Mlops Developer (Kolkata)
🏢 Tata Consultancy Services
📍 Kolkata