Job Description

We are seeking an experienced MLOps/LLMOps Architect with deep expertise in building scalable, production-grade machine learning and generative AI pipelines. The ideal candidate will have strong experience in observability, model performance monitoring , and using tools such as Arize to ensure the reliability and scalability of AI/ML models in production. This role requires a strategic mindset and hands-on technical skills to design and implement robust MLOps/LLMOps frameworks, ensuring seamless model deployment, monitoring, and optimization.

Key Responsibilities :

• Architect and Implement MLOps/LLMOps Frameworks:
• Design and build scalable MLOps/LLMOps pipelines for model training, deployment, monitoring, and retraining.
• Establish automated CI/CD pipelines to streamline model development and deployment.
• Model Observability and Monitoring:
• Develop and implement model observability strategies using tools like Arize to track model performance, drift, and bias.
• Create real-time dashboards and alerts for proactive issue identification and resolution.
• Performance and Scalability:
• Ensure high availability, low latency, and scalability of deployed models.
• Optimize model inference and serving using best practices in distributed computing and cloud infrastructure.
• Manage and optimize compute costs for large-scale Gen AI models by implementing intelligent load balancing, autoscaling, and infrastructure tuning.
• Model Governance and Compliance:
• Establish frameworks for model versioning, auditing, and explainability to meet regulatory and business requirements.
• Ensure alignment with Responsible AI and ethical AI guidelines.
• Cross-Functional Collaboration:
• Partner with data scientists, ML engineers, platform teams, and business stakeholders to align MLOps strategies with business objectives.
• Provide technical leadership and mentorship to junior team members.

Required Skills and Qualifications :

• Experience: 10 to 15 years of experience in machine learning, MLOps, and AI model deployment in enterprise environments.
• MLOps/LLMOps Expertise: Strong background in MLOps and LLMOps, including model lifecycle management, monitoring, and automation.
• Observability Tools: Proficient in using observability platforms such as Arize, Weights & Biases, TensorBoard, MLflow , or similar tools.
• Cloud Platforms: Experience with cloud-based ML solutions (e.g., AWS, Azure, GCP ).
• Programming: Strong programming skills in Python and experience with ML frameworks such as TensorFlow, PyTorch, and Hugging Face .
• Containerization and Orchestration: Hands-on experience with Docker, Kubernetes , and distributed computing frameworks.
• Model Monitoring: Experience in detecting and mitigating model drift, bias, and data quality issues.
• Performance Tuning: Expertise in model optimization, inference acceleration, and efficient resource utilization.

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