Overview

We are seeking a skilled Machine Learning Operations Site Reliability Engineer (SRE) to join our growing AI and Engineering team.

Responsibilities

• Design, implement, and maintain enterprise-grade MLOps and LLMOps pipelines for AI and machine learning workloads.
• Build secure, scalable, and reproducible model development, testing, deployment, and monitoring frameworks.
• Support the operationalization of machine learning models and Large Language Model (LLM)-based applications.
• Develop and manage Agentic AI and Retrieval-Augmented Generation (RAG) applications in production environments.
• Implement CI/CD and CT (Continuous Training) pipelines for machine learning models and AI solutions.
• Establish observability frameworks for monitoring model accuracy, latency, drift, performance, availability, and reliability.
• Conduct model benchmarking, load testing, and performance optimization activities.
• Implement security controls, governance standards, and compliance requirements across AI platforms.
• Automate infrastructure provisioning, deployment, monitoring, and operational workflows.
• Support application lifecycle management across development, testing, staging, and production environments.
• Monitor platform health and proactively identify potential risks, bottlenecks, and reliability issues.
• Troubleshoot production incidents and participate in root cause analysis activities.
• Create and maintain technical documentation, architecture guidelines, operational runbooks, code samples, and implementation blueprints.
• Collaborate with Data Scientists, ML Engineers, Security Teams, and Platform Architects to deliver reliable AI solutions.
• Drive platform improvements focused on scalability, resiliency, operational efficiency, and automation.
• Implement best practices for model lifecycle management, versioning, governance, and auditability.
• 5-8 years of experience in Machine Learning Operations, Site Reliability Engineering, DevOps, Platform Engineering, or related domains.
• Strong hands-on experience with MLOps and Machine Learning lifecycle management.
• Expertise You'll Bring:

Qualifications

• Job Type: Full-Time Employment
• Strong understanding of LLMOps concepts and deployment of Generative AI applications.
• Knowledge of model versioning, experiment tracking, model registry, and governance frameworks.
• Strong understanding of security, compliance, and responsible AI practices.
• Hands-on expertise with automation frameworks and infrastructure management.
• Strong programming experience in Python and automation scripting.
• Hands-on experience with Kubernetes and container orchestration platforms.
• Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Understanding of CI/CD practices, Infrastructure as Code, and DevOps methodologies.
• Knowledge of multimodal AI workflows and Large Language Models.
• Strong troubleshooting, analytical, and problem-solving skills.
• Excellent communication and collaboration skills with the ability to work across cross-functional teams.
• Ability to operate effectively in fast-paced and dynamic enterprise environments.

Benefits

• Competitive salary and benefits package
• Culture focused on talent development with quarterly growth opportunities and company-sponsored higher education and certifications
• Opportunity to work with cutting-edge technologies
• Employee engagement initiatives such as project parties, flexible work hours, and Long Service awards
• Annual health check-ups
• Insurance coverage: group term life, personal accident, and Mediclaim hospitalization for self, spouse, two children, and parents
• We support hybrid work and flexible hours to fit diverse lifestyles.
• Our office is accessibility-friendly, with ergonomic setups and assistive technologies to support employees with physical disabilities.
• If you are a person with disabilities and have specific requirements, please inform us during the application process or at any time during your employment