Senior SRE Engineer (MLOps) - AI

Job Description Roles & Responsibilities Salla is looking for a Senior SRE Engineer (MLOps) to join our Salla AI team. This role focuses on running our AI and ML systems as real production systems, not side experiments owning the operational layer around models, prompts, agents, inference services, and retrieval systems. You will be responsible for enabling Agentic AI and Generative AI features to operate reliably, securely, and cost-effectively at scale within the Salla ecosystem. This role is SRE- and platform-engineering-first, with a strong emphasis on reliability, observability, safe releases, cost, and governance, while collaborating closely with engineering, data, and AI teams to give every pod a fast, safe path to production. It exists because AI systems fail differently from normal services a prompt change can behave like a code change, an agent calling tools needs auditability, and latency, quality, and cost can move together in uncomfortable ways. Desired Candidate Profile 4+ years in SRE, platform engineering, DevOps, or production infrastructure, operating distributed systems in production not only in demos Hands-on experience with Kubernetes and cloud-native systems in production Familiarity with deploying ML projects Strong command of CI/CD, GitOps, observability, and incident response Solid experience with infrastructure-as-code, secrets management, and networking Ability to write automation or platform tooling in Python, or a similar language Production judgment knowing how to make systems measurable, debuggable, repeatable, and safe to change (you do not need to be a machine learning researcher) Ability to work across teams, explain trade-offs clearly, and turn operational pain into standards engineers will actually use Nice to have: Experience with MLOps or ML platforms model serving, registries, evaluation, feature/data dependencies, drift monitoring, or ML pipelines Familiarity with LLM applications or agentic systems RAG, vector databases, tool calling, workflow orchestration, memory, traces, guardrails, or evaluation pipelines Exposure to tooling such as OpenTelemetry, Prometheus, Grafana, MLflow, KServe, Ray, LiteLLM, vLLM, LangGraph, Arize Phoenix, or LangSmith Experience with Kafka consumers, GPU workloads, inference optimization, model routing, or AI cost governance Experience working in cross-functional product teams involving AI, backend, and frontend engineers Company Industry InternetE-commerceDotcom Department / Functional Area Engineering Keywords Senior SRE Engineer (MLOps) - AI Get real-time job updates only on our App