Senior MLOps EngineerWe are seeking a skilled Senior MLOps Engineer to join our team and drive the deployment, automation, and optimization of machine learning models in production. This role requires hands-on expertise in MLOps, cloud infrastructure, and pipeline automation, as well as experience working with distributed teams. If you are passionate about scaling AI solutions and ensuring model reliability in real-world applications, this opportunity is for you.Key ResponsibilitiesModel Deployment & InfrastructureDesign, build, and manage scalable, cloud-based infrastructures for deploying machine learning models in production environments (GCP, Azure).Develop and maintain CI/CD pipelines tailored for ML/NLP model deployments.Utilize Kubernetes and Docker for containerization and orchestration of models.Implement versioning, governance, and monitoring tools using MLOps frameworks such as MLFlow, Kubeflow, or DVC.Ensure compliance and security best practices in handling sensitive data.Pipeline AutomationDevelop and maintain automated workflows for training, validation, deployment, and retraining of ML models.Work closely with data engineers and data scientists to streamline data preparation, model training, and evaluation processes.Implement mechanisms for automated model retraining based on performance metrics and evolving datasets.Monitoring & MaintenanceBuild and integrate real-time monitoring systems to track model performance and detect issues such as drift and degradation.Optimize ML pipelines and infrastructure for high availability, fault tolerance, and scalability.Collaborate with engineering teams to resolve production issues related to models and pipelines.Collaboration & LeadershipMentor junior MLOps engineers and provide technical guidance.Partner with data scientists and ML engineers to ensure a smooth transition of models from development to production.Contribute to Agile processes, participating in Scrum ceremonies and fostering a culture of continuous improvement.Required Skills & ExperienceMLOps & InfrastructureProven experience deploying machine learning models into production and managing their lifecycle.Hands-on experience with MLOps tools (MLFlow, Kubeflow, DVC, Weights and Biases, etc.).Strong knowledge of cloud platforms (GCP preferred, Azure also relevant).Proficiency in Kubernetes and Docker for deploying and managing containerized applications.Experience with infrastructure as code (Terraform, Helm) for cloud resource management.Understanding of GPU-accelerated computing for large-scale model inference.Automation & DevelopmentExpertise in automating CI/CD pipelines for ML workflows using tools like Jenkins, GitLab CI/CD, or similar.Strong programming skills in Python, with additional experience in Scala and/or Java.Experience with ML frameworks and libraries, as well as distributed computing systems (Spark).Knowledge of software development best practices, including BDD/TDD and API development in Python.Collaboration & LeadershipExperience mentoring and guiding engineering teams within an agile environment.Strong communication skills, with the ability to collaborate effectively across teams and time zones.Preferred ExperienceExperience with feature stores, embeddings, LLMs, and Retrieval-Augmented Generation (RAG) architectures.Optimization of ML models for specialized hardware, including GPUs.Familiarity with DevOps principles and automation tools for infrastructure management.Strong adherence to software testing methodologies (BDD/TDD).Knowledge of parallel computing concepts and high-performance computing (HPC).What We OfferCompetitive salary Performance-based company bonusOngoing professional development opportunitiesSubscription to wellness apps