DevOpsMLOps Specialist

Education and Work Experience Requirements:  Bachelors or Masters degree in Computer Science Engineering Data Science or a related field.5 years of experience in MLOps with at least 2 years working specifically with LLMOps pipelines for large language experience in deploying managing and optimizing machine learning models in cloud environments at with AI and Generative AI technologies is a plus.       Strong problem-solving abilities and a passion for building scalable production-grade AI systems.       Lead the design and implementation of end-to-end MLOps and LLMOps pipelines from model development and training to deployment and continuous monitoring in production environments.       Develop automation for model deployment and management ensuring scalability efficiency and cost-effectiveness particularly for large language models (LLMs) in cloud environments.       Implement and manage CI/CD pipelines for both ML and LLM models integrating them seamlessly into the production environment.       Collaborate with data scientists ML engineers and software developers to ensure the integration of LLMs and ML models into scalable production-ready systems.       Build data pipelines and workflows for training and serving models ensuring efficient data processing and pipeline orchestration using tools like Kubeflow Airflow or MLflow.       Create robust monitoring systems for tracking model performance diagnosing issues and ensuring the reliability and health of both traditional ML and LLM-based systems.       Optimize the performance of deployed models focusing on cost optimization speed and accuracy including hardware acceleration (GPUs TPUs).       Maintain version control governance and model lifecycle management for both ML and LLM models using industry-standard tools.       Stay up-to-date with the latest advancements in MLOps LLMOps and Generative AI implementing best practices and innovative approaches to improve the deployment lifecycle. Mandatory Skills:         MLOps & LLMOps Expertise: Solid understanding of MLOps best practices and LLMOps workflows for large language models ensuring seamless integration of AI systems into production environments.       Cloud Infrastructure & Deployment: Extensive experience with cloud platforms (AWS GCP Azure) for deploying and scaling ML/LLM solutions. Expertise in containerization (Docker Kubernetes) for model deployment and orchestration.       CI/CD Pipelines & Automation: Proficiency in developing and managing CI/CD pipelines for machine learning models using tools like Jenkins GitLab CI or CircleCI and automation tools like Terraform or Ansible.       Model Serving & Monitoring: Hands-on experience deploying models using frameworks such as TensorFlow Serving TorchServe Kubeflow or MLflow. Strong understanding of model monitoring and alerting (e.g. Prometheus Grafana).       Machine Learning Frameworks: Strong knowledge of popular machine learning frameworks like TensorFlow PyTorch Hugging Face and others with a particular focus on deploying and fine-tuning large language models (e.g. GPT BERT).       Data Engineering & Pipelines: Expertise in building and managing robust data pipelines using tools like Apache Kafka Apache Airflow or Apache Beam to support ML and LLM workflows.       Performance Optimization: Ability to optimize the performance of AI models including working with distributed systems model compression and hardware acceleration (GPUs TPUs).       Security & Governance: Deep understanding of security and governance best practices for AI systems including model versioning compliance with privacy regulations (GDPR) and ethical AI frameworks.       Programming & Scripting: Strong programming skills in Pyth

Qualifications :

BE MCA MTech BTech

Additional Information :

6

Remote Work :

No

Employment Type :

Full-time