successfactors
MLOps engineer
Standard Chartered
Bengaluru, IND
Full-time
Senior
Onsite
Discovered 1 weeks ago
PythonSQLKubernetesLangChainLangGraphCI/CD
Free
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PythonSQLKubernetes
Role Overview
• Design, build and operate secure, reliable and scalable machine learning and Generative AI platforms across the model lifecycle. • Enable data science and engineering teams to move models from experimentation to controlled production through automation, reusable standards and self-service capabilities. • Embed observability, resilience, security, risk controls, cost efficiency and auditability into ML and AI services to protect business outcomes.
Key Skills for This Role
PythonSQLKubernetesLangChainLangGraphCI/CD
Full Job Posting
Job Summary
• Design, build and operate secure, reliable and scalable machine learning and Generative AI platforms across the model lifecycle. • Enable data science and engineering teams to move models from experimentation to controlled production through automation, reusable standards and self-service capabilities. • Embed observability, resilience, security, risk controls, cost efficiency and auditability into ML and AI services to protect business outcomes.
Key Responsibilities
- Platform Engineering and Automation • Engineer reusable CI/CD and CT pipelines for model, prompt, configuration and infrastructure releases, including automated testing, approval gates, versioning and rollback. • Build and maintain containerised workloads, orchestration patterns, model serving components, registries and environment promotion processes using infrastructure as code. • Develop standard templates and paved-road capabilities that accelerate delivery while reducing operational variation.
- Production Reliability and Operations • Operate production ML and GenAI services against agreed availability, latency, throughput and recovery objectives. • Implement telemetry for infrastructure, data, model and application health, including alerting, incident response, root-cause analysis and continuous reliability improvement. • Plan capacity and optimise compute, storage, accelerator and inference costs without compromising service quality or controls.
- Technical Leadership and Collaboration • Act as an individual contributor with technical leadership responsibility, setting engineering standards, reviewing designs and code, mentoring colleagues and improving team capability. • Work with product and business stakeholders to translate service objectives into prioritised platform capabilities and measurable operational outcomes.
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