Build our future together
Regeneron is founded on the belief that the right idea, combined with the right team, can lead to significant transformations. Our growing global network is dedicated to inventing, developing, and commercializing medicines that change lives for those with serious diseases.
At Regeneron Digital & Technology, 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.
When & Where
Hyderabad (Hybrid)
Discover your role
Drive service reliability, availability, and performance across multi-cloud environments by establishing SLOs, SLIs, error budgets, and reliability standard methodologies.
Design, build, and scale enterprise ML platform infrastructure using technologies such as Dataiku, Amazon SageMaker AI, Databricks, and Google Vertex AI.
Develop AI-powered observability solutions using anomaly detection, predictive analytics, and automated remediation to proactively identify and resolve operational issues.
Lead the implementation, evaluation, and monitoring of LLMs, SLMs, RAG pipelines, and AI Agent platforms, ensuring performance, governance, scalability, and operational efficiency.
Design and implement Infrastructure as Code (IaC), CI/CD pipelines, self-healing systems, and automation capabilities that improve engineering productivity and platform resilience.
Architect enterprise ChatOps solutions integrating AI/ML operations, observability platforms, operational events, and automated remediation workflows.
Partner with Data Science, AI Engineering, and Platform teams to deliver secure, scalable, and production-ready AI/ML platforms and services.
Evaluate emerging AI-native operational technologies and AI-assisted engineering solutions to improve platform reliability and operational excellence.
Conduct technical debt assessments, identify architectural risks, and provide strategic recommendations to modernize enterprise platforms.
Serve as a technical leader and trusted advisor, mentoring engineers and influencing SRE, MLOps, cloud platform, and AI operations strategy across the organization.
This role requires
Bachelor's degree in Computer Science, Information Technology, Engineering, Data Science, Artificial Intelligence, or a related field; Master's degree preferred.
6-8 years of experience in Site Reliability Engineering, Platform Engineering, DevOps, or related technology subject areas within enterprise-scale environments.
Strong hands-on experience working across two or more cloud platforms, including AWS, GCP, and Azure.
Deep expertise with ML platform technologies such as Databricks, Amazon SageMaker AI, Dataiku, and Google Vertex AI.
Proven experience implementing end-to-end ML workflows, including model training, deployment, experiment tracking, monitoring, and pipeline orchestration.
Hands-on experience applying ML techniques such as anomaly detection, predictive analytics, time-series modeling, and operational intelligence within enterprise platforms.
Advanced proficiency with Infrastructure as Code tools, including Terraform, Pulumi, AWS CDK, and modern CI/CD automation practices.
Strong programming and scripting capabilities in Python, Go, Bash, or similar languages.
Experience building enterprise observability platforms using Prometheus, Grafana, Datadog, OpenTelemetry, distributed tracing, metrics, and logging solutions.
Proven expertise designing and implementing enterprise ChatOps architectures, operational workflows, and AI-enabled automation solutions.
Strong ability to assess technical debt,…