AI-MLOps SRE Lead Engineer

Job Title: AI-MLOps SRE Lead Engineer

Location: Hyderabad. (Hybrid)

Build our future together

We are expanding our AI and Platform Engineering capabilities to support next-generation intelligent systems, machine learning platforms, and cloud-native technologies. We are seeking an AI-MLOps SRE Lead Engineer to drive reliability, scalability, observability, and operational excellence across our AI, ML, and cloud ecosystem. This role will lead the design and operation of resilient platforms supporting machine learning workloads, LLMs, AI Agents, and enterprise-scale automation while enabling engineering teams to innovate with speed and confidence.

Discover your role

• Drive service reliability, availability, and performance across multi-cloud environments, establishing SLOs, SLIs, error budgets, and reliability standard methodologies.
• Design, build, and scale enterprise ML platform infrastructure using technologies such as Dataiku OR Amazon SageMaker AI OR Databricks OR Google Vertex AI.
• Develop AI-driven observability capabilities using anomaly detection, predictive analytics, and automated remediation solutions to proactively identify and resolve operational issues.
• Lead the implementation and monitoring of LLM, SLM, RAG, and AI Agent platforms, ensuring performance, governance, operational efficiency, and scalability.
• Design and implement Infrastructure as Code, CI/CD pipelines, self-healing systems, and platform automation capabilities to improve engineering productivity and operational resilience.
• Architect enterprise ChatOps solutions that integrate operational events, AI workflows, observability platforms, and automated remediation capabilities.
• Partner with Data Science, AI Engineering, and Platform teams to deliver secure, scalable, and production-ready AI/ML solutions.
• Evaluate emerging AI-native operational technologies and integrate innovative solutions that enhance platform reliability, engineering efficiency, and business value.
• Conduct technical debt assessments, identify architectural risks, and provide strategic recommendations to improve enterprise platform maturity.
• Serve as a technical leader and trusted advisor, mentoring engineers and influencing reliability engineering, MLOps, and cloud platform strategy across the organization.
• This role requires
• Bachelor's degree in Computer Science, Information Technology, Engineering, Data Science, Artificial Intelligence, or a related subject area; Master's degree preferred.
• 6-8 years of experience in Site Reliability Engineering, Platform Engineering, DevOps, or related technology fields with enterprise-scale delivery experience.
• Strong hands-on experience operating across two or more major cloud platforms, including AWS, GCP, or Azure.
• Deep expertise with ML platform technologies including Databricks OR Amazon SageMaker AI, OR Dataiku, OR Google Vertex AI.
• Proven experience implementing end-to-end ML workflows including model training, deployment, experiment tracking, monitoring, and pipeline orchestration.
• Advanced proficiency in Infrastructure as Code tools such as Terraform, Pulumi, AWS CDK, and modern CI/CD automation practices.
• Strong programming and scripting skills in Python, Go, Bash, or similar languages.
• Experience building enterprise observability solutions using Prometheus, Grafana, Datadog, OpenTelemetry, distributed tracing, metrics, and logging platforms.
• Demonstrated expertise in anomaly detection, predictive analytics, automated remediation, and AI-assisted operational capabilities.
• Proven experience designing and implementing enterprise ChatOps solutions and AI-enabled operational workflows.
• Strong ability to assess technical debt, influence technical strategy, and drive platform modernization initiatives.
• Experience using AI tools, LLM-powered assistants, and AI Agents to enhance engineering operations and productivity.
• Experience with Kubernetes and container orchestration platforms such as EKS, GKE, or AKS preferred.
• Familiarity with MLOps technologies including Kubeflow, Feast, MLflow, and model evaluation frameworks such as LangSmith, RAGAS, Evidently AI, or Weights & Biases preferred.
• Knowledge of cloud cost optimization, policy-as-code, compliance automation, and multi-cloud governance practices preferred.