ML Ops Engineer

Who We AreSirion is the world s leading AI-native Contract Lifecycle Management (CLM) platform, transforming the end-to-end contracting journey for global enterprises
With Agentic AI at the core, Sirion s extraction, conversational search, and AI-enhanced negotiation capabilities are redefining how Fortune 500 companies like IBM, Coca-Cola, Citi, and GE manage contracts With 800+ employees worldwide AI engineers, legal experts, and researchers we are continuously innovating to build the most reliable and trustworthy CLM for the enterprises of tomorrow Sirion is consistently recognized by Gartner, IDC, and Spend Matters as a category leader in CLM innovation To learn more, visit Power the Future of AI & Why This Role MattersAs an MLOps Engineer, you will play a critical role in enabling reliable, scalable, and governed machine learning systems in production You will be responsible for building and operating the platforms, pipelines, and infrastructure that allow ML and LLM models to move safely from experimentation to real-world deployment This role sits at the intersection of machine learning, cloud infrastructure, and platform engineering, and directly impacts the speed, quality, and trustworthiness of AI systems across the organization How You ll Make an Impact Build, automate, and maintain end-to-end MLOps pipelines, including data ingestion, preprocessing, model training, validation, deployment, and inference Design, develop, and operate CI/CD workflows for machine learning, supporting model versioning, artifact management, lineage tracking, and automated rollback strategies Create and maintain internal MLOps platforms and self-service tools that enable data scientists and ML engineers to deploy models with minimal operational overhead Deploy, manage, and optimize ML and LLM inference services in production, including GPU-accelerated workloads Establish comprehensive monitoring, alerting, and observability for model performance, data drift, concept drift, explainability, and infrastructure health Define and enforce ML governance, security, and model risk management practices, embedding auditability, compliance, and access controls into the ML platform Collaborate closely with Data Science, ML Engineering, Data Engineering, Architecture, and DevOps teams to design scalable, resilient ML infrastructure Stay current with emerging trends, tools, and best practices in MLOps, LLMOps, cloud platforms, and distributed systems, driving continuous improvement Skills & Experience You Bring to the Table 5-8+ years of hands-on experience designing, deploying, and operating production-grade ML systems Strong programming proficiency in Python, with solid Linux fundamentals and working knowledge of Go or Spark Deep understanding of the machine learning lifecycle, including training, evaluation, deployment, monitoring, and retraining Practical experience with MLOps platforms and tools such as Kubeflow, MLflow, KServe, and NVIDIA ML toolkits Proven experience deploying and optimizing LLMs in production, using technologies such as vLLM, TensorRT-LLM, DeepSpeed, or TGI Strong experience working with GPU-based environments, including performance tuning and cost optimization Expertise in cloud platforms (AWS, GCP, or Azure), containerization with Docker, and orchestration using Kubernetes Hands-on experience with CI/CD systems and Infrastructure as Code tools such as Terraform Experience with streaming and messaging technologies (Kafka or Pulsar) for real-time and event-driven ML pipelines Familiarity with vector databases and retrieval pipelines supporting RAG-based systems Strong software engineering fundamentals, including version control, automated testing, debugging, and operational reliability Excellent communication and collaboration skills, with the ability to work effectively across cross-functional teams Mandatory Skills MLOps / ML Platform Engineering Kubernetes and Docker GPU-based model deployment and optimization Cloud platforms: AWS and/or GCPPreferred Skills