About Sirius AI Sirius AI is a US-headquartered AI consulting and products company with operations in India. We work with financial services organisations across geographies to solve business problems using data, AI and technology. Our teams combine consulting, industry, product and engineering expertise to build practical solutions that deliver measurable business outcomes. Role Overview We are looking for a Manager – ML Ops to lead the design, development and operation of reliable, scalable and production ready machine learning platforms and workflows. The role will work closely with data scientists, AI engineers, software engineers and client stakeholders to take models from experimentation to production and ensure they remain reliable, productive, secure and well monitored. The ideal candidate brings strong experience in MLOps and cloud-native engineering, understands the end-to-end machine learning lifecycle, and can lead a team while remaining hands-on in architecture, implementation and technical problem solving. Exposure to LLMOps and the deployment of Generative AI applications will be an advantage.

Key Responsibilities
• Lead the design, development and implementation of end-to-end MLOps platforms, processes and deployment pipelines for machine learning solutions.
• Build and manage automated workflows for model training, testing, validation, packaging, deployment, versioning and rollback.
• Establish CI/CD and CT pipelines for ML systems, enabling repeatable releases across development, staging and production environments.
• Deploy and operate models at scale using containerization and orchestration technologies; ensure availability, performance, scalability and reliability.
• Set up monitoring for model performance, data quality, model drift, service health, latency and resource utilization, and drive timely remediation.
• Partner with data science and AI teams to productions models, improve experimentation-to-production workflows and establish reusable engineering standards.
• Build and maintain feature pipelines, data and model registries, metadata tracking, experiment tracking, and model lifecycle management capabilities.
• Drive cloud infrastructure and platform practices across environments, with a focus on security, access control, cost optimization and operational resilience.




• Define and implement best practices for infrastructure such as code, observability, logging, alerting, incident management, and disaster recovery.
• Evaluate and adopt relevant tools, frameworks and architectural patterns to improve the MLOps ecosystem and developer productivity.
• Support production deployments of Generative AI and LLM-based applications, including model serving, evaluation, monitoring, prompt/version management and operational controls, where applicable.
• Lead, mentor and develop a team of ML Ops and platform engineers; set technical direction, review designs and code, and contribute to hiring.
• Work with client and internal stakeholders to understand requirements, communicate technical trade-offs, and ensure successful delivery of engagements. What We’re Looking For
• Bachelor’s or master’s degree in computer science, Engineering or a related technical field.
• 4–8 years of experience in software engineering, DevOps, cloud engineering, ML engineering or MLOps, with substantial hands-on experience in productionising machine learning systems.
• Strong understanding of the end-to-end ML lifecycle, including data preparation, experimentation, training, validation, deployment, monitoring and retraining.
• Enable Model tracking, model experimentation, Model automation, Develop scalable ML pipelines.
• Develop MLOps components in Machine learning development life cycle using Model Repository (either of): MLFlow, Kubeflow Model Registry, Machine Learning Services (either of): Kubeflow, DataRobot, HopsWorks, Dataiku or any relevant ML EZE Paas/Saas
• Work across all phases of Model development life cycle to build MLOPS components.
• Build the knowledge base required to deliver increasingly complex MLOPS projects on the Cloud (AWS, Azure, GCP)/On Prem
• Hands-on experience building CI/CD pipelines and automated ML workflows using tools such as GitHub Actions,



GitLab CI, Jenkins, Kubeflow or similar.
• Experience with cloud platforms such as AWS, Azure or GCP and their ML and infrastructure services.
• Strong working knowledge of Docker and Kubernetes, including deploying, scaling and managing containerised workloads.
• Experience with model serving and lifecycle tools such as MLflow, Kubeflow, KServe, Seldon or equivalent platforms.
• Proficiency in Python and familiarity with SQL, APIs and software engineering practices such as version control, testing, code reviews and documentation.
• Understanding of monitoring and observability tools and practices for applications, infrastructure, data pipelines and deployed models.
• Experience with infrastructure as code and configuration management tools such as Terraform, Helm or Ansible.
• Good understanding of security, networking, access management and cost optimisation in cloud-based production environments.
• At least 2 years of experience leading or mentoring engineers, managing delivery and coordinating with cross-functional teams.
• Strong communication and problem-solving skills, with the ability to explain technical decisions and manage stakeholder expectations. Good to Have
• Experience with LLMOps and production deployment of Generative AI applications, including LLM serving, evaluation, observability and performance optimisation.
• Exposure to vector databases, retrieval-augmented generation (RAG) pipelines and orchestration frameworks used in GenAI applications.
• Experience with feature stores, data orchestration and streaming technologies such as Feast, Airflow, Kafka or Flink.
• Experience working with financial services, banking, fintech or other regulated environments.
• Exposure to model governance, auditability, explainability and model risk management requirements.
• Relevant cloud, Kubernetes or MLOps certifications. Role Expectations
• Ability to balance hands-on technical contribution with team leadership and delivery ownership.
• Comfort working in a consulting and product-oriented environment with multiple stakeholders and evolving requirements.
• Willingness to take ownership of platform reliability and production outcomes, not just initial deployment.

📌 Manager - MLOps (Gurugram)
🏢 SiriusAI
📍 Gurugram