ML Ops Engineer

Who We Are

Sirion 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.

www.sirion.ai

Power the Future of AI & Why This Role Matters

MLOps Engineer

machine learning, cloud infrastructure, and platform engineering

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 GCP

Preferred Skills

• Experience working on

  AI/ML or GenAI-driven production systems

• Exposure to

  ML governance, compliance, or model risk management frameworks

Education

• BE / BTech / MCA or equivalent degree from a

  UGC-accredited university

Excited about this opportunity?

Career at Sirion