About SiriusAI 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 Senior Consultant – ML Ops with 2–4 years of experience to support the development, deployment and maintenance of scalable, reliable and production-ready machine learning solutions. The role involves working closely with data scientists, AI engineers, software engineers and other stakeholders to automate ML workflows, deploy models into production and ensure their performance, reliability and scalability. The ideal candidate should have strong hands-on experience in MLOps, Python, cloud platforms, CI/CD pipelines and containerization technologies, along with a good understanding of the end-to-end machine learning lifecycle. Exposure to LLMOps and Generative AI applications will be an added advantage.

Key Responsibilities
• Develop, implement and maintain MLOps pipelines and workflows to support the end-to-end machine learning lifecycle.
• Build and automate workflows for model training, testing, validation, packaging, deployment, versioning and monitoring.
• Implement and maintain CI/CD and Continuous Training (CT) pipelines for ML applications across development, staging and production environments.
• Deploy and manage machine learning models using Docker, Kubernetes and cloud-based infrastructure.
• Monitor model performance, data quality, model drift, latency and system health to ensure reliable production operations.
• Collaborate with data scientists and engineering teams to productionize ML models and streamline deployment processes.
• Work with model registries, experiment tracking tools and model lifecycle management platforms such as MLflow and Kubeflow.




• Support cloud infrastructure setup, configuration and maintenance across AWS, Azure or GCP environments.
• Implement logging, monitoring, alerting and troubleshooting mechanisms for ML applications and infrastructure.
• Assist in developing reusable MLOps components, automation scripts and deployment frameworks.
• Support the deployment and monitoring of Generative AI and LLM-based applications, where applicable.
• Work with internal teams and client stakeholders to understand technical requirements, resolve issues and ensure timely delivery. What We’re Looking For
• Bachelor’s or master’s degree in computer science, Engineering or a related technical field.
• 2–4 years of relevant experience in MLOps, ML Engineering, DevOps, Cloud Engineering or Software Engineering, with hands-on exposure to deploying machine learning solutions.
• Good understanding of the end-to-end ML lifecycle, including data preparation, model training, experimentation, validation, deployment, monitoring and retraining.
• Hands-on experience in model tracking, model experimentation, model automation and developing scalable ML pipelines.
• Experience working with MLOps tools and model repositories such as MLflow, Kubeflow Model Registry or equivalent platforms.
• Exposure to machine learning platforms such as Kubeflow, DataRobot, HopsWorks, Dataiku or similar ML PaaS/SaaS solutions.
• Experience developing and maintaining CI/CD pipelines using GitHub Actions, GitLab CI, Jenkins or similar tools.
• Working knowledge of at least one major cloud platform: AWS, Azure or GCP.




• Hands-on experience with Docker and working knowledge of Kubernetes for containerized application deployment.
• Familiarity with model serving and lifecycle management tools such as MLflow, Kubeflow, KServe or Seldon.
• Strong programming skills in Python, along with working knowledge of SQL, APIs, Git and software engineering best practices.
• Understanding of monitoring, logging, observability and troubleshooting for deployed ML models and applications.
• Exposure to infrastructure-as-code tools such as Terraform, Helm or Ansible.
• Basic understanding of cloud security, access management, networking and cost optimization.
• Good analytical, problem-solving and communication skills, with the ability to collaborate effectively across teams. Good to Have
• Exposure to LLMOps and deployment of Generative AI applications, including LLM serving, monitoring and evaluation.
• Familiarity with vector databases, Retrieval-Augmented Generation (RAG) pipelines and GenAI orchestration frameworks.
• Experience with feature stores, data orchestration or streaming technologies such as Feast, Airflow, Kafka or Flink.
• Exposure to financial services, banking, fintech or other regulated industries.
• Understanding of model governance, auditability, explainability and model risk management.
• Relevant certifications in AWS, Azure, GCP, Kubernetes or MLOps. Role Expectations
• Robust hands-on technical capabilities with a willingness to learn and work across different MLOps tools and technologies.
• Ability to independently execute assigned development, deployment and troubleshooting tasks.
• Comfortable working in a consulting and product-oriented environment with multiple stakeholders and evolving requirements.
• Focus on writing clean, maintainable and reusable code while following engineering best practices.
• Ownership of assigned deliverables, production support activities and continuous improvement of ML workflows.

📌 Senior Consultant (Gurugram)
🏢 SiriusAI
📍 Gurugram